Anais do Workshop de Visão Computacional (WVC)
Sociedade Brasileira de Computação - SBC
Pig aggression classification using CNN, Transformers and Recurrent Networks
The recognition of behavioral relationships related to aggression in pigs is an important task that is visually observed in the livestock industry. However, this task is laborious and susceptible to errors, which can be reduced through automation by visually classifying videos captured in a controlled environment. Video classification can be automated using techniques from computer vision and artificial intelligence, employing neural network methods. The primary techniques utilized in this study are variants of transformers: STAM, TimeSformer, and ViViT, as well as techniques using convolutions, such as ResNet3D2, ResNet(2+1)D, and CnnLstm. These techniques were compared to analyze their individual contributions. The performance was evaluated using metrics such as accuracy, precision, and recall. The TimeSformer technique demonstrated the most promising results in video classification, achieving a median accuracy of 0.729.
Detecting Mechanical Vibrations in Televisions via Audio Spectrogram Classification
This paper presents a method for contactless detec tion of mechanical vibrations in televisions through audio spec trogram classification, utilizing Convolutional Neural Networks. The model was trained on a dataset containing simulated samples and demonstrated high accuracy, with excellent learning curves observed during training. In further evaluation with real samples the model performed well, achieving F1-Score rate of 99,02% in the test partition, confirming its potential for use in preventive maintenance processes and in addressing issues in televisions and other audio-dependent equipment, thereby enhancing the efficiency and quality of service.
Anomaly Detection in Television Digital Channel
Detecting anomalies in industrial processes is a field in constant advancement. However, automating this task presents significant challenges due to the complexity of the problem. In this paper, Deep Learning techniques were employed to detect anomalies in video footage during digital channel testing of televisions on a production line. A 3D Convolutional Neural Network was trained on a dataset containing two classes of videos: those with simulated defects and those without defects. The resulting model achieved an accuracy of 98,45% with a processing speed of 648 FPS.
Detection of breast cancer in infrared images using Discrete Wavelet Transform and Support Vector Machine
This work presents a comparative study between two approaches which aim at helping diagnosing patients with breast cancer using thermal images. The first method uses the Discrete Wavelet Transform (DWT) for feature extraction and Support Vector Machine (SVM) to classify the patients. The other method is based on feature extraction with pretrained convolutional neural networks in conjunction with deep neural networks to perform the classification. VGG16, ResNet50 and MobileNet were considered to determine which was best suited to classify patient infrared images. Experimental results showed that DWT in conjunction with SVM presented the best performance on classifying 928 images (489 healthy and 429 sick) with 98% accuracy, 97% sensibility and 98% specificity.
Image-based Semantic Segmentation Network for the Brazilian Cerrado based on Public Databases
We have developed fully convolutional networks (FCN) for semantic segmentation of satellite imagery based on five Land Use and Land Cover (LULC) categories: native vegetation, agriculture, pasture, urban region, and waterbody. To this end, we gathered and preprocessed public Brazilian data into an annotated dataset with 26,000 segmented 224 × 224 image patches. We obtained images from the Sino-Brazilian CBERS-04A satellite program and segmentation masks from the Terra Class project (INPE/Embrapa). We performed transfer learning on four backbone models: DeepLabV3+, MobileNet, ResNet-50, and VGG-16. We evaluated their performance with IoU, with the respective scores of 45.96%, 34.40%, 45.58%, and 62.78%. However, our dataset is unbalanced, and a balanced IoU yields scores lower than 20% for all models, indicating specialization on majority classes. Despite these shortcomings, our models’ masks have a 16-times higher pixel-density than previously available masks, and taking only images as input, without external data or expert curation.
Altitude Prediction Over Water Surface Using CNN Trained With Synthetic Images
The need for redundant odometry in robotic systems benefits from the recent possibilities of fast and efficient real-time image processing using classifications and regression models. The present work proposes an approach using Convolutional Neural Networks (CNN) to detect the distance between the point of capture of an image and a water surface below the capture. Our CNN was trained initially with synthetic images generated by Blender, and evaluated by real images at heights between 50 and 100 cm. Preliminary tests demonstrated an ability of the system to recognize the changing of height in a continuous shot, varying only the vertical position, even with some noise from elements not present in the training test database, such as dirt from leaves and stones at the bottom of the water.
Detection of Cacao Trees in Orthomosaic Images Using YOLOv8 and SAHI
This study explores the application of drone-based orthomosaic imagery combined with the YOLOv8x segmentation model and the Slicing Aided Hyper Inference (SAHI) method for detecting cacao trees. High-resolution orthomosaic images were processed by slicing them into smaller tiles to facilitate object segmentation. Although the YOLOv8x model was trained for segmentation, the evaluation was conducted using bounding boxes for ease of comparison. The model achieved a precision of 93.49% and an accuracy of 81.10%, demonstrating its potential for monitoring large-scale plantations. However, the system is best suited for estimation purposes rather than precise, real-time decision-making.
Automated Detection of Pineapple Plants in Drone-Captured Aerial Imagery for Precision Agriculture
Pineapple harvesting remains largely manual due to scattered planting patterns and complex fruit structure. This study presents a method for detecting pineapple plants in large orthomosaic images using the Slicing Aided Hyper Inference (SAHI) technique combined with the YOLOv8 segmentation model. SAHI divides large images into smaller patches, enabling accurate detection. A dataset of 867 training and 97 validation images from two orthomosaics was used, with the model achieving 93% precision and 88% accuracy. Despite high precision, chal lenges with false negatives suggest future improvements. This approach shows promise for automating pineapple harvesting and improving agricultural efficiency.
Vitor Matheus Soares Siqueira De Almeida
Transfer Learning and Handcrafted Features Ensembles for Ultrasound Breast Cancer Image Classification
Breast cancer is the most commonly diagnosed cancer in women. Its diagnosis via ultrasound imaging largely depends on the technical skill of the radiologist. This study developed a binary classification system for breast lesions, combining transfer learning models and handcrafted features in ultrasound images. Pre-trained CNNs like InceptionV3, EfficientNetB4, ResNet50, and VGG16 were used, along with SVM-classified handcrafted features. Models were individually analyzed and combined using late-fusion ensembles. ResNet50 achieved an F1-score of 81.97%. The best late-fusion ensemble model reached an F1-score of 83.90%. In the cross-dataset evaluation, the top late-fusion ensemble model in the development dataset scored an F1-score of 88.70% and 78.20% in the test BUSI and BUID datasets, respectively. These results emphasize the robust potential of a late-fusion ensemble that combines CNN transfer learning and handcrafted features to classify breast lesions in ultrasound images.
Classification of Agricultural Pests Through Digital Images Using Deep Learning
In the agricultural sector, pest detection is vital, and given the susceptibility of human analysis to errors, deep learning solutions such as Convolutional Neural Networks (CNNs) provide a promising alternative. Classifying insect pests is challenging due to the high variability among species across different regions and their various life stages. In this study, we evaluate several deep learning models and training strategies for automatic pest image classification. We analyze four CNN architectures—AlexNet, ResNet-50, EfficientNet, and Vision Transformer (ViT). Following a hyperparameter optimization step, the models were fine-tuned, and we examined the impact of four data augmentation strategies on classification performance using the Agricultural Pests dataset. ViT demonstrated superior performance, achieving an accuracy of 0.9574 without data augmentation. Although ViT did not benefit from data augmentation, these techniques proved essential for enhancing the performance of AlexNet, ResNet-50, and EfficientNet. Our findings underscore the potential of deep learning methods for pest classification, offering valuable tools to help professionals maintain crop quality and value.
Application of Denoising Diffusion Probabilistic Methods in Fetal MRI
Magnetic resonance imaging (MRI) is a common type of medical image acquisition that can also be used to diagnose early diseases. In this sense, fetal MRI is a non-invasive method to generate high-quality fetal volumes and to perform important clinical analysis. However, image denoising is necessary in many situations to ensure accurate evaluations. Thus, approaches such as Denoising Diffusion Probabilistic Methods (DDPM) have emerged and reached great results in this kind of task. In this work, we applied two DDPM-based approaches (an original and an improved one) besides two self-supervised deep models in a fetal MRI dataset. The results showed that, for the used fetal MRI dataset, the improved DDPM, named I-DDPM outperformed the counterparts considering two evaluation metrics for image quality, the Peak Signal-to-Noise Ratio (PSNR) and Root Mean Squared Error (RMSE).
Preprocessing and Optimizer impact on Image-Based Autoimmune Disease Diagnosis
The precise diagnosis of autoimmune diseases is challenging due to overlapping symptoms. Despite significant advancements in techniques based on Convolutional Neural Networks (CNNs), research opportunities remain in evaluating the impact of external factors on CNN performance. This study investigates CNN behavior by focusing on two factors: image preprocessing and optimizer choice. We assessed the effects of contrast stretching and histogram equalization on HEp-2 images and compared the Adam and SGD optimizers. Using a factorial experimental design, we trained and validated CNN models on a dataset from the International Conference on Pattern Recognition (ICPR-2014) competition, categorized into six classes of HEp-2 cells. This study contributes to understanding how different factors influence CNN models in medical image processing.
Composite Material Defect Segmentation Using Deep Learning Models and Infrared Thermography
For non-destructive assessment, the segmentation of infrared thermographic images of carbon fiber composites is a critical task in material characterization and quality assessment. This study focuses on applying image processing techniques, particularly adaptive thresholding, alongside neural network models such as U-Net and DeepLabv3 for infrared image segmentation tasks. An experimental analysis was conducted on these networks to compare their performance in segmenting artificial defects from infrared images of a carbon-fibre reinforced polymer sample. The performance of these models was evaluated based on the F1-Score and Intersection over Union (IoU) metrics. The findings reveal that DeepLabv3 demonstrates superior results and efficiency in segmenting patterns of infrared images, achieving an F1-Score of 0.94 and an IoU of 0.74, showcasing its potential for advanced material analysis and quality control.
Automatic Detection and Counting of Tuta Absoluta Insect in Trap Images
The integration of artificial intelligence (AI) into agriculture offers solutions to challenges such as pest control. AI can improve productivity and sustainability through precision agriculture. This paper presents an automatic system for identifying and counting Tuta absoluta pests in trap images, integrated into a monitoring platform. The platform uses a biological defensive system for sustained pest control. The solution employs ImageAI deep learning algorithms to detect and classify pests, using YOLOv3 and TinyYOLOv3 models. We provide assessments of the performance and resource consumption of the evaluated models. YOLOv3 achieved a detection precision of 95.28% in images with 10-50 insects, decreasing to 87.51% for around 100 insects. Despite YOLOv3 demonstrating higher precision in the detection of the number of insects, the Tiny YOLOv3 model was shown to be 4.5 times faster in the training process and occupies almost 8 times less storage space.
Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning
Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentation. These methods can be deployed in both centralized and distributed environments, depending on the computational demands of the task. While much of the literature has focused on the explainability of CNNs, which is essential for building trust and confidence in their predictions, there remains a gap in understanding their impact on computational resources, particularly in distributed training contexts. In this study, we analyze how CNN architectures primarily influence model accuracy and investigate additional factors that affect computational efficiency in distributed systems. Our findings contribute valuable insights for optimizing the deployment of CNNs in resource-intensive scenarios, paving the way for further exploration of variables critical to distributed learning.
Renal Cancer Classification in Tomographic Images Using Convolutional Neural Networks to Assist Early Diagnosis
Renal cancer is one of the 13 most frequent types of cancer in Brazil, primarily affecting individuals between 50 and 70 years of age, with around 12,000 cases and 4,000 deaths recorded in 2020. Early detection is crucial to prevent the progression of the disease, which can lead to kidney transplants, dialysis, or death. However, the lack of symptoms and screening tests make diagnosis difficult. This research proposes the use of Convolutional Neural Networks (CNNs) for the detection of renal cancer in CT images, classifying them as cancerous or normal. It is expected that the application of the obtained results can assist in early diagnosis, supporting healthcare professionals in providing faster and more accurate treatments and contributing to improving the patients' quality of life.
Automatic Identification of Relevant Moments in Security Force Videos Using Multimodal Analysis
Due to the increasing requirement for police officers to wear body cameras, there is an increased need for algorithms that can automatically detect relevant moments in footage. This paper presents an automated system that uses audio and video inputs to highlight key events in recordings, reducing the need for operators to watch entire videos. Our method detects firearms and crowd gatherings with object detection, identifies people raising their hands with pose estimation. We also detect sound patterns such as sirens, gunshots, and shouts and use Automatic Speech Recognition to transcribe conversations and identify keywords for relevant events. Our system, evaluated with videos from YouTube channels such as PMTVSP, PoliceActivity, and Code Blue Cam, effectively identifies significant moments in security footage where agents are engaged in activities beyond routine patrol, thus avoiding the need for a human to watch the entire video to point out relevant moments.
Portal to Other Dimensions: use of Computer Vision to create art work from day life images
This work aims to combine classic and contemporary paintings with the external world by altering the interior of a door into another style in real-time photos and/or videos, creating a portal effect to provide an interactive experience with artworks. To achieve this, various techniques from Artificial Intelligence and Computer Vision were employed, primarily focusing on convolutional neural networks (CNNs) with supervised training. Models proposed for semantic segmentation problems were used for door detection and style transfer to stylize the input image. Finally, we conclude with an analysis of the results for each network individually and in combination, ensuring that the metrics and approaches were satisfactory. With our results, we were able to offer an accessible and interactive way to bring art into the daily lives of many people.
Deep Learning-Based Segmentation of Nanomaterial Images Acquired via Scanning Electron Microscopy
This work investigates the application of deep learning techniques to analyze nanomaterial images acquired by scanning electron microscopy (SEM). The main objective is to improve the segmentation of zinc oxide (ZnO) nanoparticles and graphene oxide (GO) nanosheets. For this purpose, we developed a dedicated database for the supervised training of deep neural networks, using the U-Net architecture. Given the high spatial resolution of the original images, we investigated two approaches for image subdivision during the training and testing stages. Due to the high cost of acquiring and annotating SEM images of nanomaterials, we applied transfer learning through fine-tuning, adapting the network previously trained on ZnO images to segment GO images. The results were promising, with accuracies and recalls exceeding 95% for both ZnO and GO images.
Artificial Intelligence for Gas Leak Detection with Thermal Cameras and Metal Oxide Semiconductor Sensors
Early detection of gas leaks is crucial for safety and efficiency in oil platforms and refineries. The presence of various hazardous gases, often imperceptible to human senses, poses significant risks. AI-powered solutions can effectively monitor for gas leaks, improving safety and ensuring efficient operations. In this work we proposed a modular architecture effectively combines tabular data from gas sensors and spatial information from thermal images using a variety of backbones, including MobileNet. By employing dense layers and an optimized training strategy, we achieved state-of-the-art performance, with 100% accuracy, demonstrating the effectiveness of our approach for gas leakage detection.
Segmentation of Skin Lesions and their Attributes in Dermatoscopic Images Based on Convolutional Neural Networks
Segmentation of skin lesions in dermoscopic images is a crucial step in diagnosing skin cancer, and Convolutional Neural Networks (CNNs) have emerged as powerful tools to address this challenge. This paper evaluated the effectiveness of two CNN models, TernausNet-16 and Mask R-CNN, in segmenting skin lesions and five of their attributes in the dermatoscopic images from the ISIC 2018 Challenge dataset. Jaccard Similarity Index (JSI) and Dice Similarity Coefficient (DSC) have been used as evaluation metrics. The results revealed that Mask R-CNN significantly outperformed TernausNet-16. The best model achieved 82.57% JSI and 84.76% DSC for lesion segmentation, and 42.86% JSI and 51.60% DSC for attribute segmentation when addressing attribute imbalance. Despite the longer training time, the results highlighted the potential of Mask R-CNN for improving the effectiveness of melanoma segmentation.
Sheep Behavioral Analysis with Optical Flow
Determining behavioral deviations in sheep is challenging through visual observation alone, as sheep are generally stoic animals and may not easily show signs of pain or discomfort. This study aimed to evaluate and classify activity changes in sheep using optical flow algorithms as an alternative approach. Video recordings of 32 sheep housed in 8 pens were analyzed over 4 days, with each day consisting of 4 one-hour video segments. These segments were manually observed at 5-minute intervals to document the time spent laying, standing, and walking. Simultaneously, the same video segments were analyzed using optical flow algorithms to track movement patterns. The calculated optical flow measures, including mean, variance, skewness, and kurtosis, were then correlated with the observed behaviors. These measures demonstrated their potential as effective tools for monitoring behavioral activity changes in sheep, providing an objective method to track behavior and detect deviations that might indicate discomfort, stress, or health issues.
Bulldogs Nose Detection using Deep Learning
An animal nose identification system could allow efficient monitoring of pets, and it can be used in applications such as identifying animal breeds or possible diseases and/or injuries. For that, a bulldog image dataset was build from French bulldogs. To carry out the validation through cross-validation, 10 folds (K-folds) were created. Afterward, five convolutional neural networks (CNN) were trained with our dataset to identify the nose: Faster R-CNN (Region-based CNN), SABL (Side-Aware Boundary Localization), RetinaNet (ResNet50+FPN), VFNet (VarifocalNet), and ATSS (Adaptive Training Sample Selection). Faster R-CNN, SABL, RetinaNet, VFNet and ATSS were used for training in the first phase, while ATSS, Faster R-CNN, ATSS and SABL in the second. The results showed that the ATSS network obtained the highest values of mAP and Accuracy in the first phase. Moreover, SABL network achieved the highest values of mAP50, mAP75, Recall, F-Score and Accuracy at the end of the second phase.
Fusion of Satellite and Drone Images for Identifying Water Bodies
Mapping areas with water bodies is crucial for monitoring water systems and resource management, as well as for guiding urban expansion and land-use planning, thus contributing to sustainable resource management, flood prevention, and the conservation of aquatic ecosystems. While satellite images offer broad territorial coverage, drones provide high spatial resolution of the Earth's surface. In this research, the Pulse Coupled Neural Network (PCNN) [1] image fusion method and the GDAL Pansharpening method, the latter being available in QGIS, were compared to determine which is more effective in merging images to highlight water surface areas in a case study, combining the spectral coverage of satellites with the precision of drones. The implemented PCNN model was able to overcome QGIS Pansharpening method, which is based on Brovey algorithm, obtaining better metrics. This study of drone and satellite image fusion has shown to be a promising approach to overcome the limitations of these technologies when used individually.
Classification of Cells Infected by Malaria with Vision Transformers Models
This project develops a system for malaria classification using the Vision Transformer architecture on cellular tissue microscopy images. The implementation aims to assist in medical diagnosis by providing efficient and accurate information, with the potential to contribute to the control of endemic diseases. The system utilizes models from the vit-pytorch library, including the Vision Transformer, Patch Merger, and Vision Transformer for Small Datasets. The Vision Transformer for Small Datasets achieved the highest accuracy of 98.97 for an image size of [150,150].
Deep Learning-Based Instance Segmentation for Enhanced Navigation of Agricultural Vehicles
This paper presents the development of a computer vision application based on the YOLOv8 network, designed to assist the navigation of autonomous vehicles on rural roads, particularly those found in sugarcane fields. The application employs instance segmentation to differentiate between navigable and non-navigable areas and detect obstacles such as pedestrians, vehicles, and other potential hazards. This information is used to generate an occupancy map that helps the navigation planner identify the safest and most efficient routes. The system was trained on a dataset containing 1.018 images, and the results demonstrate that instance segmentation significantly enhances the precision and safety of autonomous navigation in complex rural environments. The proposed approach is compatible with the ROS2 framework, using its structure for data integration and enabling real-time decision making.
Safer Stack: Safe Dump of Off-Highways Trucks in Slope Crest Windrows
During operation, the dump truck sometimes needs to operate close to a slope crest windrow. This article aims to present the experimentation of a system, called Safer Stack, to assist the dump truck operator when reversing in front of a slope crest windrow, with the purpose of informing him the safe distance to perform the dump and generating alerts when there is a risk. The system includes computer vision, through image recognition, and distance measurement, through a LiDAR. The information will be used in a graphical interface, with visual alerts and audible alarms for the dump truck operator. Based on the tests carried out, it was confirmed that the combination of technologies in a final solution, since it presented 98% accuracy in the trained scenarios, has the potential to generate highly efficient results and make the operation safer.
A Novel Brazilian Dataset for Automatic Detection of Psyllid Attacksin Guava
Guava, a fruit native to America, plays a crucial role in the Brazilian economy, with production reaching 564,000 tons by 2022. It is widely used in culinary dishes and herbal remedies, and is exported to numerous countries. However, guava is also susceptible to pests and diseases. Among these, Psyllid Triozoda Limbata is particularly problematic, attacking young leaves and leading to reduced fruit yields and impaired plant development. To address this issue and assist producers in detecting pest infestations, this study introduces a novel dataset comprising images from Brazil. By leveraging Artificial Intelligence and Computer Vision techniques, this dataset provides a foundation for developing pest detection systems. We evaluated various object detection architectures including different versions of YOLO and Vision Transformer (ViT). The experiments indicated that YOLO V5 achieved promising results, with a precision of 93.14%. Moreover, the availability of this dataset opens new opportunities for enhancing pest management strategies, improving crop protection, and increasing the overall productivity in the guava industry.
YOLO and CNN for Cat Detection and Recognition
This paper proposes a processing architecture for recognizing domestic felines in videos and images based on facial features. Some photos of the cats to be identified in the videos and images need to be collected in advance. A key aspect of this study is avoiding the need to retrain the model whenever the set of cats to be identified changes. The architecture involves five processing stages: video processing, YOLOv8 for cat detection, InceptionV3 for feature extraction, KNN for database searching, and voting process to improve classification. Transfer learning and fine-tuning techniques are employed to improve performance. The proposed method achieves high accuracy, offering a scalable and non-invasive solution for cat recognition, which can contribute to urban management, cat loss prevention, and medical insurance control for domestic cats, among other applications.
Case Study of Deep Learning Methods for Depth Estimation in Indoor Ground Robotics
Depth estimation is the computer vision task that assigns a distance between the camera and each pixel in an image. This paper focuses on monocular metric depth estimation in videos, which infers a distance in metric units using a single RGB camera. Considering its applications, robotics systems and environmental mapping arise as practical areas that can make extensive usage of these techniques. As a case study for indoor robotics, the ICL ground robot dataset obtained by video footage in graphic simulation was used for experiments. A comparison was made considering the results and requirements of data acquisition needed for different deep learning models, presenting self-supervised and supervised methods available in literature and being the first work to present a depth estimation benchmark for the chosen dataset.
Federated Learning in Breast Cancer Diagnosis
Breast cancer, which accounts for 29.7% of cancer cases among women and has a mortality rate of approximately 16.5%, it is the most common cancer in Brazil. To enhance early detection and minimize the occurrence of advanced cases, pathologists are increasingly utilizing deep learning techniques to analyze histopathological images. This study investigates and compares the performance of two convolutional neural networks, AlexNet and ResNet50, in federated and centralized training settings. By employing the BreakHis dataset, the research explores the effectiveness of federated learning in securely distributing model training across multiple clients and contrasts it with traditional centralized training approaches. This study investigates whether federated learning can achieve performance close to that of centralized approaches while ensuring privacy for breast cancer histopathology image classification. It is hypothesized that federated learning can provide comparable results to centralized approaches while ensuring the confidentiality of patient data. Our results confirm that federated learning performs similarly to centralized learning with the added benefits of maintaining privacy and supporting collaboration among multiple clients. The findings aim to advance the state of the art in breast cancer detection, addressing both the challenges of privacy and data distribution and the impact of different training methodologies on model performance.
Medical Image Classification with Privacy: Centralized and Federated Learning Comparison
The use of different techniques in image-based medical diagnostic computing has positively impacted the healthcare industry. Diseases such as colon and lung cancer can be detected using image classification strategies. In this context, Federated Learning (FL) has emerged as an enabling approach to disease detection with security and privacy when handling sensitive patient data. This study aimed to compare the centralized and FL classifications of histopathological images of lung and colon cancer. We compared the performance of FL with that of centralized learning, and the results indicated that FL outperformed centralized approaches in scenarios with heterogeneous datasets. These findings highlight the potential of FL as an alternative to medical image classification, particularly in privacy-sensitive applications.
Construction of Rolled Digital for Newborns Using Image Registration
This work aimed to reconstruct rolled fingerprint images from multiple flat fingerprints using image registration and composition techniques. The methodology involved selecting high-quality frames through a quality network, the application of preprocessing with remapping, cropping the area of interest, and adjusting colors. In addition to image registration steps, like key point identification, feature matching, and image distortion. Results indicated that while classical image registration had limitations, the composition technique significantly increased the number of features extracted, enhancing fingerprint identification. It was demonstrated that the composition method effectively captures important fingerprint details, offering a potential improvement in newborn biometric identification.
Digital Fencing for Farms: Enhancing Object Detection through Multi-Dataset Integration
Security in farm areas is a critical concern due to the risks posed by wild animal invasions, farm animals straying, and unauthorized human entry. Conventional object detection models, often trained on urban or context-specific datasets, frequently underperform in farm settings. To address this, our study investigates the integration of multi-datasets to enhance object detection for digital fences, thereby improving farm security. We propose a method to effectively utilize multi-datasets, even when they do not have the same classes, ensuring comprehensive coverage of all required categories. The proposed SmartClass methodology achieved more robust and adaptable detection approaches, suitable for agricultural environments, with considerable increases in recall, mAP50, and mAP50-95 metrics compared to models trained without the methodology. The code and data are available at github.com/MaVILab-UFV/Digital-Fencing-WVC-2024.
Juliana Quintiliano de Oliveira Ferreira
Identifying Malignant Skin Diseases Through Deep Learning
Early and accurate identification of malignant skin diseases is critical to improving treatment outcomes and patient prognosis. Current diagnostic methods heavily rely on visual assessments by medical professionals, followed by biopsies for confirmation. These approaches can be subjective and require significant expertise, while access to specialized care remains limited, particularly in developing regions. Recent advancements in artificial intelligence, specifically deep learning, present an opportunity to improve dermatological diagnostics. This paper explores the application of two deep learning models, ResNet and Vision Transformer (ViT), for the automated detection of malignant skin conditions. Both models were trained on the PAD-UFES-20 dataset, which comprises clinical skin lesion images captured via smartphones. Our findings suggest that these models can offer real-time diagnostic support at reduced costs, with accuracy levels comparable to or exceeding human performance, with the best experiment achieving the F1 score of 79.89% and an accuracy of 80.63%.
Rust Detection in Coffee Leaves Using Otsu's Method and Mathematical Morphology
Coffee leaf rust is a major threat to global coffee production, causing significant yield and quality losses. Traditional detection methods rely on manual inspection, which is labor-intensive, time-consuming, and susceptible to human error. This study presents an automated system for detecting coffee leaf rust using image processing techniques, combining Otsu's thresholding method with mathematical morphology to accurately identify rust-affected areas in digital images. The methodology addresses challenges such as variability in image quality, noise, and segmentation accuracy. Using a dataset of manually annotated images, the system's performance was evaluated with the Dice coefficient, demonstrating its effectiveness. Finally, we compare the results with a method that uses genetic algorithms to achieve the same goal, obtaining good results despite using a much simpler process.
Castor Beans Identification in Sugarcane Plantations Using Active Learning
Castor beans have multiple applications but can become weeds in several crops. Manual identification is unfeasible in large plantations. Machine learning models can make identification efficient but require a lot of labeled data. Many images of a plantation would be similar and wouldn't improve the model's performance. Active learning (AL) allows labeling only relevant data, often surpassing the performance of models trained on entire datasets. In this work, we detect castor beans, with an AL method that uses self-supervised pretext tasks to separate data for labeling. Models based on pretext tasks presented a decrease in recall relative to the model trained in the whole dataset, which has 93%. We also trained a pseudo-task that separates data with a reasonable concentration of castor bean images. The pseudo-task classifier obtained a 92% recall, being trained in less than 1% of the dataset.
A New Approach for Soil Carbon and Nitrogen Assessment through Image Analysis: A Case Study with Oxisols and Inceptisols
This paper presents a new method for predicting carbon and nitrogen content in Oxisols and Inceptisols soils using image analysis. The dataset comprises 40 Oxisols images and 51 Inceptisols images collected in southern Brazil. The images were segmented to isolate the soil area, resulting in the extraction of 12,744 windows. A pre-trained EfficientNetV2-S model was fine-tuned for regression, adding five layers at the end. The model was evaluated using RMSE and R² metrics. Two experiments were conducted: one predicting carbon and nitrogen content for each window and another predicting the median values of the windows for each soil sample. The results show that the median-based prediction significantly improved model performance, achieving an R² of 0.7425 for carbon and 0.7774 for nitrogen. This method offers a faster, non-destructive alternative to laboratory analysis for estimating soil carbon and nitrogen content, contributing to sustainable agricultural practices.
A Mini-map Alignment Approach using Landmark Observation Information Intrinsic to the Feature-based Visual SLAM Pipeline
This work proposes a map alignment approach based on a simple modification of the Iterative Closest Point algorithm to use point confidences based on metrics usually available in feature-based visual SLAM pipelines. In the context of a hierarchical map composed of mini-maps, aligning these mini-maps is an important task to allow metric information to be related between them. This research enumerates three possible SLAM metrics that could be used for representing landmark confidence, and investigate the potential of using these metrics to improve the ICP algorithm. The experiments show evidence that the usage of the confidence metrics might help to improve the convergence of ICP with only a small modification to the data association step.
Deep Semantic Segmentation Applied to Honey Bee Comb Cells
Machine learning is increasingly prevalent in various fields, including beekeeping, where it has been applied to honeycomb analysis through Semantic Segmentation of images. This study evaluates different neural network architectures and encoders using the Intersection over Union (IoU) method to segment Apis mellifera honeycombs. A comparative analysis of semantic segmentation techniques was conducted, revealing advancements in the field with the SegNet architecture combined with the MobileNet encoder for feature extraction. We present a new dataset of 10 comb cell images, which we combine with the images from the DeepBee dataset in our experiments. Our results surpass the state-of-the-art results for comb cell segmentation in the DeepBee dataset.
Leveraging Optimal Methods for Diverse Skin Types Classification in Images for Reduced Bias
This paper addresses the bias in automatic skin tone classification, highlighting how technologies can mirror societal issues such as inadequate representation of diverse skin tones. The objective is to evaluate methods that ensure balanced performance across skin types despite a scarcity of annotated databases with ethnic-racial details. Using the Fitzpatrick Skin Type classification system, we assess various skin color labeling algorithms, selecting optimal approaches for each type based on f-score. Results show that a single method biases results towards certain skin tones, whereas combining methods enhances accuracy across diverse types. We leverage the LFW and Fitzpatrick17k databases and apply image processing techniques like gamma transformation, CLAHE, histogram equalization, and non-linear order statistics filters. By tailoring processes to specific skin tone ranges, our auto-labeling approach better mirrors manual labeling, aiming for more equitable technology.
Exploiting Data Augmentation Strategies to Improve the Classification of Spinal Disorders in X-Ray Images
Spinal disorders affect a significant portion of the population and are a growing concern for healthcare authorities due to their potentially debilitating consequences. In this context, computer vision techniques offer a promising path for rapid diagnosis, but they often require large datasets to train robust and reliable models. However, public datasets that can support the training of such models are scarce. To address this, we explored the application of advanced data augmentation strategies, namely CutMix, CutOut, and MixUp, combined with standard augmentation techniques to improve the performance of deep learning models in classifying X-ray images into three categories: (i) healthy, (ii) scoliosis, and (iii) spondylolisthesis. We applied these techniques by training the ResNet-50, Vision Transformer (ViT), and Swin Transformer V2 architectures, evaluating their effectiveness for this task. Our experiments revealed that the combination of ViT architecture and CutMix augmentation achieved the highest accuracy, with a performance of 0.9882.
Evaluating Deep Learning Models for Effective Weed Classification in Agricultural Images
Effective weed management is crucial for maximizing agricultural productivity and minimizing crop losses. Traditional methods for weed detection and classification often suffer from errors and delays, particularly in the absence of specialists. Deep learning technologies offer a promising alternative for automating these tasks, potentially enhancing both accuracy and efficiency. This study compares three advanced deep learning architectures, ResNet-50, EfficientNet V2, and Vision Transformers (ViT), for classifying weed species using the DeepWeeds dataset. We explore the effects of data augmentation on model performance, evaluating each model based on accuracy, precision, recall, and F1 score. Our results demonstrate that data augmentation significantly improves model performance. EfficientNet V2 achieved the highest performance across all metrics, with a peak accuracy of 0.9703. This research provides valuable insights into selecting effective architectures and training strategies for more accurate weed detection in agricultural applications.
How Misleading Can The Analysis of Human Eye Movements Be? Experiments on Dominante Eye and Cognitive Visual Perception
Research on the human dominant eye has existed for over 100 years, yet no consistent methodology uses dominant eye data to analyze eye movements in eye-tracking studies. This is concerning, as eye-tracking technology is increasingly important for understanding human visual perception, especially in healthcare decision-making. The aim of this project is to develop an approach for analyzing the dominant eye using Tobii TX300 eye-tracking equipment. The specific objectives are: (1) to create a fast, non-invasive test to determine the dominant eye; (2) to develop a signal acquisition system with various stimuli and pattern recognition; and (3) to analyze results based on dominant eye outcomes. Initial results show divergence between the eyes, but the mean of fixation points from both eyes provides the most accurate mathematical metric with the lowest quadratic errors compared to theoretical fixation points.
Analyzing Bee Behavior Through Video Tracking Using Computer Vision Techniques
Bees are responsible for a large part of the pollination of plant species and act as bioindicators in evaluating phytosanitary products. Research on bee behavior and mortality can be carried out in laboratories with a controlled environment. Still, even so, behavioral assessment, when performed visually by an expert, becomes time-consuming and susceptible to failures, which justifies the creation of automated systems for this purpose. This article presents a tool based on Computer Vision to support this activity. The YOLO/Darknet architecture was used to detect bees in videos, and experiments were carried out with eight pre-trained models, obtaining adequate results of insect detection, which were used to develop an interface that provides graphs and reports: heat maps, walking maps, the percentage of the area explored, the time spent moving, and the speed at which individuals move. Experts can interpret the information produced by the proposed tool to understand bee behavior.
Aedes aegypti Egg Counting with Neural Networks for Object Detection
Aedes aegypti is still one of the main concerns when it comes to disease vectors. Among the many ways to deal with it, there are important protocols that make use of egg numbers in ovitraps to calculate indices, such as LIRAa and Breteau Index, which can provide information on predictable outbursts and epidemics. Also, there are many research lines that require egg numbers, specially when mass production of mosquitoes is needed. Egg counting is a laborious and error-prone task that can be automated via computer vision-based techniques, specially deep learning-based counting with object detection. In this work, we propose a new dataset comprising field and laboratory eggs, along with test results of three neural networks applied to the task: Faster R-CNN, Side-Aware Boundary Localization and FoveaBox. With FoveaBox, we achieve a median mean absolute error of 6.854. Finally, we also discuss the main difficulties and possibilities for future research.