Análise Comparativa de Modelos de Detecção para Identificação de Ovelhas em Ambientes Naturais
Resumo
O monitoramento automatizado de rebanhos é uma estratégia essencial para a pecuária moderna, pois permite otimizar recursos, reduzir falhas humanas e melhorar a tomada de decisão no campo. Este trabalho tem como objetivo comparar diferentes arquiteturas de redes neurais convolucionais aplicadas à detecção de animais em ambientes naturais, visando identificar a solução mais eficiente. Foram avaliados quatro modelos amplamente utilizados na literatura: YOLOv8, SSD, RetinaNet e Faster R-CNN. O desempenho foi medido por meio de métricas padronizadas, como precisão, recall, mAP e tempo de inferência. Os resultados demonstraram que o YOLOv8 obteve o melhor desempenho geral, destacando-se pela alta acurácia e velocidade de processamento. Conclui-se que o YOLOv8 é a abordagem mais adequada para aplicações em pecuária de precisão com foco em detecção visual em tempo real.Referências
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Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, pages 91–99.
Ross, T.-Y. and Dollár, G. (2017). Focal loss for dense object detection. In proceedings of the IEEE conference on computer vision and pattern recognition, pages 2980–2988.
Schneider, S., Taylor, G. W., and Kremer, S. (2018). Deep learning object detection methods for ecological camera trap data. In 2018 15th Conference on computer and robot vision (CRV), pages 321–328. IEEE.
Silva, J. e. a. (2021). Aprendizagem profunda para monitoramento de fauna com drones. Revista de Visão Computacional.
SILVA, V. J. P. d. et al. (2021). Aprendizagem de máquina aplicada ao monitoramento da presença de animais em reservas naturais de ambientes industriais.
Thomas, M. S., George, E., Francis, A., Job, A., and James, A. M. Wildlife detection and recognition using yolo v8.
Baoyuan, C., Yitong, L., and Kun, S. (2021). Research on object detection method based on ff-yolo for complex scenes. IEEE Access, 9:127950–127960.
Bjerge, K., Alison, J., Dyrmann, M., Frigaard, C. E., Mann, H. M., and Høye, T. T. (2023). Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustainability and Transformation, 2(3):e0000051.
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338.
Galvez, R. L., Bandala, A. A., Dadios, E. P., Vicerra, R. R. P., and Maningo, J. M. Z. (2018). Object detection using convolutional neural networks. In TENCON 2018-2018 IEEE region 10 conference, pages 2023–2027. IEEE.
Henderson, P. and Ferrari, V. (2017). End-to-end training of object class detectors for mean average precision. In Computer Vision–ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part V 13, pages 198–213. Springer.
Jiang, L. and Wu, L. (2024). Enhanced yolov8 network with extended kalman filter for wildlife detection and tracking in complex environments. Ecological Informatics, 84:102856.
Kumar, A., Zhang, Z. J., and Lyu, H. (2020). Object detection in real time based on improved single shot multi-box detector algorithm. EURASIP Journal on Wireless Communications and Networking, 2020(1):204.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 2980–2988.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C. (2016). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision (ECCV), pages 21–37.
Liu, Y., Che, S., Ai, L., Song, C., Zhang, Z., Zhou, Y., Yang, X., and Xian, C. (2024). Camouflage detection: Optimization-based computer vision for alligator sinensis with low detectability in complex wild environments. Ecological Informatics, 83:102802.
Menon, H. P., Vinitha, V., Vishnuraj, K., Satheesh, A., Nikhil, A., et al. (2023). A study on yolov5 for drone detection with google colab training. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), pages 1576–1580. IEEE.
Neupane, J. e. a. (2022). A literature review on deep learning applications for livestock monitoring. Computers and Electronics in Agriculture.
Neupane, S. B., Sato, K., and Gautam, B. P. (2022). A literature review of computer vision techniques in wildlife monitoring. IJSRP, 16:282–295.
Nguyen, H., Maclagan, S. J., Nguyen, T. D., Nguyen, T., Flemons, P., Andrews, K., Ritchie, E. G., and Phung, D. (2017). Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring. In 2017 IEEE international conference on data science and advanced Analytics (DSAA), pages 40–49. IEEE.
Oksuz, K., Cam, B. C., Akbas, E., and Kalkan, S. (2018). Localization recall precision (lrp): A new performance metric for object detection. In Proceedings of the European conference on computer vision (ECCV), pages 504–519.
Padilla, R., Netto, S. L., and Da Silva, E. A. (2020). A survey on performance metrics for object-detection algorithms. In 2020 international conference on systems, signals and image processing (IWSSIP), pages 237–242. IEEE.
Park, M. J. and Sacchi, M. D. (2020). Automatic velocity analysis using convolutional neural network and transfer learning. Geophysics, 85(1):V33–V43.
Powers, D. M. W. (2020). Evaluation: From precision, recall and f-measure to roc, informedness, markedness correlation. Journal of Machine Learning Technologies.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, pages 91–99.
Ross, T.-Y. and Dollár, G. (2017). Focal loss for dense object detection. In proceedings of the IEEE conference on computer vision and pattern recognition, pages 2980–2988.
Schneider, S., Taylor, G. W., and Kremer, S. (2018). Deep learning object detection methods for ecological camera trap data. In 2018 15th Conference on computer and robot vision (CRV), pages 321–328. IEEE.
Silva, J. e. a. (2021). Aprendizagem profunda para monitoramento de fauna com drones. Revista de Visão Computacional.
SILVA, V. J. P. d. et al. (2021). Aprendizagem de máquina aplicada ao monitoramento da presença de animais em reservas naturais de ambientes industriais.
Thomas, M. S., George, E., Francis, A., Job, A., and James, A. M. Wildlife detection and recognition using yolo v8.
Publicado
20/07/2025
Como Citar
SANTOS NETO, João dos; MARQUES, Júlio Vitor Monteiro; SARMENTO, Jose Lindenberg Rocha; VELOSO E SILVA, Romuere Rodrigues.
Análise Comparativa de Modelos de Detecção para Identificação de Ovelhas em Ambientes Naturais. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 52. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 310-320.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2025.8507.
