Visão Computacional Aplicada à Identificação e Segmentação de Animais: Uma Revisão da Literatura
Resumo
A Visão Computacional tem se tornado fundamental para a Pecuária de Precisão ao automatizar tarefas, como a contagem e a estimativa de peso de animais, de forma não invasiva. Diante desse contexto, este artigo apresenta uma revisão da literatura sobre Visão Computacional aplicada à identificação e segmentação de animais. A partir de um protocolo de pesquisa, foram analisados 15 artigos, publicados entre 2020 e 2025. Os resultados apontam para a predominância de modelos customizados para superar desafios práticos, como oclusão de animais e limitações de hardware. Uma contribuição deste trabalho é a identificação de lacunas na literatura acerca de estudos de detecção e segmentação de animais, baseados em YOLO.
Palavras-chave:
Visão Computacional, Pecuária de Precisão, Detecção de Objetos, Segmentação de Instâncias, YOLO
Referências
Antognoli, V. et al. (2025) “Computer Vision in Dairy Farm Management: A Literature Review of Current Application and Future Perspectives”, Animals, v. 15, n. 17, 2508.
Berckmans, D. (2017) “General introduction to precision livestock farming”, Animal Frontiers, Oxford, v. 7, n. 1, p. 6-11.
Bernardi, A. C. C. et al. (2022) “Tecnologias de pecuária de precisão para o manejo de pastagens e animais”. In: Agricultura de Precisão: Um novo olhar na era digital. Brasília: Embrapa. p. 623-635.
Carranza-Garcia, M. et al. (2021) “On the Performace of One-Stage and two-Stage Object Detectors in Autonomous Vehicles Using Camera Data”, Remote Sensing, v. 13, n, 89.
CEPEA – Centro de Estudos Avançados em Economia Aplicada. (2025) “PIB do Agronegócio Brasileiro”. Disponível em: l1nq.com/Lcb4M. Acesso em: 1 out. 2025.
Cui, Y. et al. (2023) “Research on broiler health status recognition method based on improved YOLOV5”, Smart Agricultural Technology, v. 6, 100324.
Elmessery, W. M. et al. (2023) “YOLO-Based Model for Automatic Detection of Broiler Pathological Phenomena Though Visual and Thermal Images in Intensive Poultry House”, Agriculture, v.13, n.8, 1527.
Goodfellow, I.; Bengio, Y.; Courville, A. (2016) “Deep Learning”, Cambridge: MIT Press.
He, K. et al. (2017) “R-CNN”. In: IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), Veneza. Proceedings [...]. Veneza: IEEE. p. 2961-2969.
He, K. et al. (2016) “Deep residual learning for image recognition”. In: IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), Las Vegas. Proceedings [...]. Las Vegas: IEEE. p. 770-778
He, P. et al. (2024) “PDC-YOLO: A Network for Pig Detection Under Complex Conditions for Counting Purposes”, Agriculture, v. 14, n. 10, 1807.
Krizhevky, A.; Sutskver, I.; Hinton, G. E. (2012) “ImageNet Clssification with Deep Convulutional Neural Networks”. In: Conference on Neural Information Processing Systems (NIPS), 2012. Lake Tahoe. Proceedings [...]. Lake Tahoe: Curran Associates. p. 1097–1105.
Lecun, Y.; Bengio, Y.; Hinton, G. (2015) “Deep Learning”, Nature, v. 521, n. 7553, p. 436-444.
Li, C. et al. (2025) “RFR-YOLO-Based Recognition Method for Dairy Cow Behavior in Farming Environments”, Agriculture, v. 15, n. 18, p. 1952.
Li, D. et al. (2024) “Cow-YOLO: Automatic Cow Mounting Detection Based on Non-Local CSPDarknet53 and Multiscale neck”, International Journal of Agricultural and Biological Engineering, v. 17, n. 3, p. 193-202.
Liao, M et al. (2025) “Predicting Dairy Calf Body Weight from Depth Images Using Deep Learning (YOLOv8) and Threshold Segmentation with Cross-Validation and Longitudinal Analysis”, Animals, v. 15, n. 6, 868.
Liao, Y. et al. (2025) “YOLOv8A-SD: A Segmentation-Detection Algorithm for Overlooking Scenes in Pig Farms”, Animals, v.15, n.7, 1000.
Liu, S. et al. (2025) “An Improved YOLOv8-Based Lightweight Attention Mechanism for Cross-Scale Feature Fusion”, Remote Sensing, v. 17, n. 6, 1044.
Long, T. et al. (2025) “FSCA-YOLO: An Enhanced YOLO-Based Model for Multi-Target Dairy Cow Behavior Recognition”, Animals, v. 15, n. 17, 2631.
Luo, Y. et al. (2025) “A lightweight model for automatic pig counting in intensive piggeries using a green inspection robot and image segmentation method”, Smart Agricultural Technology, v. 12, 101115.
Luo,Y. et al. (2025) “PBR-YOLO: A Lightweight Piglet Multi-behavior Recognition Algorithm Based on Improved YOLOv8”, Smart Agricultural Technology, v.10, 100785.
Menezes, G. L. et al. (2024) “Artificial Intelligence for Livestock: A Narrative review of the Applications of Computer Vision Systems and Large Language models for Animal Farming”, Animal Forntiers, v. 14, n. 6, p. 43-53.
Pan, S. J.; Yang, Q. (2010) “A survey on transfer learning”, IEEE Transactions on Knowledge and Data Engineering, v. 22, n. 10, p. 1345-1359.
Redmon, J. et al. (2016) “You Only Look Once: Unifies, Real Time Object Detection”. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas. Proceedings [...]. Las Vegas: IEEE. p. 779-788.
Ren, S. et al. (2016) “Faster R-CNN: Towards real-time object detection with region proposal networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 39, n. 6, p. 1137-1149.
Ronneberger, O.; Fischer, P.; Brox, T. (2015) “U-Net: Convolutional Networks for Biomedical Image Segmentation”. In: INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION (MICCAI), Munique. Proceedings [...]. Lecture Notes in Computer Science, v. 9351. Munique: Springer. p. 234–241.
Shams, M. Y. et al. (2025) “Automated on-site broiler live weight estimation through YOLO-based segmentation”, Smart Agricultural Technology, v. 10, p. 100828.
Silva, A. M. et al. (2023) “Uso de Inteligência Artificial na Pecuária: Revisão da literatura”, Research, Society and Development, v.12, n.4 e6612440777.
Sun, S. et al. (2024) “Nondestructive estimation method of live chicken leg weight based on deep learning”, Poultry Science, v. 103, 103477.
Tan, M.; Le, Q. V. (2019) “EfficientNet: Rethinking model scaling for convolutional neural networks”. In: INTERNATIONAL CONFERENCE ON MACHINE LEARNIG (ICML), 36., Long Beach. Proceedings [...]. Long Beach: PMLR. p. 6105-6114.
Tong, L. et al. (2024) “Research on Cattle Behavior Recognition and Multi-Object Tracking Algorithm Based on YOLO-BOT”, Animals, v. 14, n. 20, 2993.
Zhang, Y. et al. (2025) “DualHet-YOLO: A Dual-Backbone HetoroGeneous YOLO Network for Inspection Robots to Recognize Yellow-Feathered Chicken Behavior in Floor-Raised House”, Agricuture, v. 15, n.14, 1504.
Zheng, Z.; Li, J.; Qin, L. (2023) “YOLO-BYTE: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows”, Agriculture, v. 209, 107857.
Berckmans, D. (2017) “General introduction to precision livestock farming”, Animal Frontiers, Oxford, v. 7, n. 1, p. 6-11.
Bernardi, A. C. C. et al. (2022) “Tecnologias de pecuária de precisão para o manejo de pastagens e animais”. In: Agricultura de Precisão: Um novo olhar na era digital. Brasília: Embrapa. p. 623-635.
Carranza-Garcia, M. et al. (2021) “On the Performace of One-Stage and two-Stage Object Detectors in Autonomous Vehicles Using Camera Data”, Remote Sensing, v. 13, n, 89.
CEPEA – Centro de Estudos Avançados em Economia Aplicada. (2025) “PIB do Agronegócio Brasileiro”. Disponível em: l1nq.com/Lcb4M. Acesso em: 1 out. 2025.
Cui, Y. et al. (2023) “Research on broiler health status recognition method based on improved YOLOV5”, Smart Agricultural Technology, v. 6, 100324.
Elmessery, W. M. et al. (2023) “YOLO-Based Model for Automatic Detection of Broiler Pathological Phenomena Though Visual and Thermal Images in Intensive Poultry House”, Agriculture, v.13, n.8, 1527.
Goodfellow, I.; Bengio, Y.; Courville, A. (2016) “Deep Learning”, Cambridge: MIT Press.
He, K. et al. (2017) “R-CNN”. In: IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), Veneza. Proceedings [...]. Veneza: IEEE. p. 2961-2969.
He, K. et al. (2016) “Deep residual learning for image recognition”. In: IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), Las Vegas. Proceedings [...]. Las Vegas: IEEE. p. 770-778
He, P. et al. (2024) “PDC-YOLO: A Network for Pig Detection Under Complex Conditions for Counting Purposes”, Agriculture, v. 14, n. 10, 1807.
Krizhevky, A.; Sutskver, I.; Hinton, G. E. (2012) “ImageNet Clssification with Deep Convulutional Neural Networks”. In: Conference on Neural Information Processing Systems (NIPS), 2012. Lake Tahoe. Proceedings [...]. Lake Tahoe: Curran Associates. p. 1097–1105.
Lecun, Y.; Bengio, Y.; Hinton, G. (2015) “Deep Learning”, Nature, v. 521, n. 7553, p. 436-444.
Li, C. et al. (2025) “RFR-YOLO-Based Recognition Method for Dairy Cow Behavior in Farming Environments”, Agriculture, v. 15, n. 18, p. 1952.
Li, D. et al. (2024) “Cow-YOLO: Automatic Cow Mounting Detection Based on Non-Local CSPDarknet53 and Multiscale neck”, International Journal of Agricultural and Biological Engineering, v. 17, n. 3, p. 193-202.
Liao, M et al. (2025) “Predicting Dairy Calf Body Weight from Depth Images Using Deep Learning (YOLOv8) and Threshold Segmentation with Cross-Validation and Longitudinal Analysis”, Animals, v. 15, n. 6, 868.
Liao, Y. et al. (2025) “YOLOv8A-SD: A Segmentation-Detection Algorithm for Overlooking Scenes in Pig Farms”, Animals, v.15, n.7, 1000.
Liu, S. et al. (2025) “An Improved YOLOv8-Based Lightweight Attention Mechanism for Cross-Scale Feature Fusion”, Remote Sensing, v. 17, n. 6, 1044.
Long, T. et al. (2025) “FSCA-YOLO: An Enhanced YOLO-Based Model for Multi-Target Dairy Cow Behavior Recognition”, Animals, v. 15, n. 17, 2631.
Luo, Y. et al. (2025) “A lightweight model for automatic pig counting in intensive piggeries using a green inspection robot and image segmentation method”, Smart Agricultural Technology, v. 12, 101115.
Luo,Y. et al. (2025) “PBR-YOLO: A Lightweight Piglet Multi-behavior Recognition Algorithm Based on Improved YOLOv8”, Smart Agricultural Technology, v.10, 100785.
Menezes, G. L. et al. (2024) “Artificial Intelligence for Livestock: A Narrative review of the Applications of Computer Vision Systems and Large Language models for Animal Farming”, Animal Forntiers, v. 14, n. 6, p. 43-53.
Pan, S. J.; Yang, Q. (2010) “A survey on transfer learning”, IEEE Transactions on Knowledge and Data Engineering, v. 22, n. 10, p. 1345-1359.
Redmon, J. et al. (2016) “You Only Look Once: Unifies, Real Time Object Detection”. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas. Proceedings [...]. Las Vegas: IEEE. p. 779-788.
Ren, S. et al. (2016) “Faster R-CNN: Towards real-time object detection with region proposal networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 39, n. 6, p. 1137-1149.
Ronneberger, O.; Fischer, P.; Brox, T. (2015) “U-Net: Convolutional Networks for Biomedical Image Segmentation”. In: INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION (MICCAI), Munique. Proceedings [...]. Lecture Notes in Computer Science, v. 9351. Munique: Springer. p. 234–241.
Shams, M. Y. et al. (2025) “Automated on-site broiler live weight estimation through YOLO-based segmentation”, Smart Agricultural Technology, v. 10, p. 100828.
Silva, A. M. et al. (2023) “Uso de Inteligência Artificial na Pecuária: Revisão da literatura”, Research, Society and Development, v.12, n.4 e6612440777.
Sun, S. et al. (2024) “Nondestructive estimation method of live chicken leg weight based on deep learning”, Poultry Science, v. 103, 103477.
Tan, M.; Le, Q. V. (2019) “EfficientNet: Rethinking model scaling for convolutional neural networks”. In: INTERNATIONAL CONFERENCE ON MACHINE LEARNIG (ICML), 36., Long Beach. Proceedings [...]. Long Beach: PMLR. p. 6105-6114.
Tong, L. et al. (2024) “Research on Cattle Behavior Recognition and Multi-Object Tracking Algorithm Based on YOLO-BOT”, Animals, v. 14, n. 20, 2993.
Zhang, Y. et al. (2025) “DualHet-YOLO: A Dual-Backbone HetoroGeneous YOLO Network for Inspection Robots to Recognize Yellow-Feathered Chicken Behavior in Floor-Raised House”, Agricuture, v. 15, n.14, 1504.
Zheng, Z.; Li, J.; Qin, L. (2023) “YOLO-BYTE: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows”, Agriculture, v. 209, 107857.
Publicado
12/11/2025
Como Citar
ANTUNES, Leonardo J.; SANTOS, Matheus; CUNHA, Henrique S.; SILVA, Juliana S..
Visão Computacional Aplicada à Identificação e Segmentação de Animais: Uma Revisão da Literatura. In: ESCOLA REGIONAL DE INFORMÁTICA DE MATO GROSSO (ERI-MT), 14. , 2025, Pontes e Lacerda/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 158-167.
ISSN 2447-5386.
DOI: https://doi.org/10.5753/eri-mt.2025.17241.
