YOLOv5-VF-W3: A novel cattle body detection approach for precision livestock farming
Abstract
Key words: cattle body detection; varifocal loss; key body parts; WIoUv3 loss
DOI: 10.25165/j.ijabe.20251802.9107
Citation: Hao W L, Ren C, Han M, Li F Z, Liu Z Y. YOLOv5-VF-W3: A novel cattle body detection approach for precision livestock farming. Int J Agric & Biol Eng, 2025; 18(2): 269–277.
Keywords
Full Text:
PDFReferences
Guo H, Ma X D, Ma Q, Wang K, Su W, Zhu D H, et al. An interactive 3D point clouds analysis software for body measurement of livestock with similar forms of cows or pigs. Computers and Electronics in Agriculture, 2017; 138: 60–68.
Mao A, Huang E D, Wang X S, Liu K. Deep learning-based animal activity recognition with wearable sensors: Overview, challenges, and future directions. Computers and Electronics in Agriculture, 2023; 211: 108043.
Awad A I. From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, 2016; 123: 423–435.
Roberts C M. Radio frequency identification (RFID). Computers and Security, 2006; 25(1): 18–26.
Islam M A, Lomax S, Doughty A K, Islam M R, Thomson P C, Clark C E F, et al. Revealing the diversity in cattle behavioural response to high environmental heat using accelerometer-based ear tag sensors. Computers and Electronics in Agriculture, 2021; 191: 106511.
Dutta D, Natta D, Mandal S, Ghosh N. MOOnitor: An IoT based multi-sensory intelligent device for cattle activity moni-toring. Sensors and Actuators A: Physical, 2022; 333: 113271.
Hossain M E, Kabir M A, Zheng L H, Swain D L, McGrath S, Medway J. A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions. Artificial Intelligence in Agriculture, 2022; 6: 138–155.
Bercovich A, Edan Y, Alchanatis V, Moallem U, Parmet Y, Honig H, et al. Development of an automatic cow body condition scoring using body shape signature and Fourier descriptors. Journal of Dairy Science, 2013; 96(12): 8047–8059.
Zhao K X, Jin X, Ji J T, Wang J, Ma H, Zhu X F. Individual identification of Holstein dairy cows based on detecting and matching feature points in body images. Biosystems Engineering, 2019; 181: 128–139.
Gao T. Detection and tracking cows by computer vision and image classification methods. International Journal of Security and Privacy in Pervasive Computing, 2021; 13(1): 45.
Liu D, Zhao K X, He D J. Real-time target detection for moving cows based on gaussian mixture model. Transactions of the Chinese Society for Agricultural Machinery, 2016; 47: 288–294. (in Chinese)
Kaur A, Kumar M, Jindal M K. Shi-Tomasi corner detector for cattle identification from muzzle print image pattern. Ecological Informatics, 2022; 68: 101549.
Tassinari P, Bovo M, Benni S, Franzoni S, Poggi M, Mammi L M E, et al. A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Computers and Electronics in Agriculture, 2021; 182: 106030.
Lodkaew T, Pasupa K, Loo C K. CowXNet: An automated cow estrus detection system. Expert Systems with Applications, 2023; 211: 118550.
Xiao J X, Liu G, Wang K J, Si Y S. Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM. Computers and Electronics in Agriculture, 2022; 194: 106738.
Redmon J, Farhadi A. YOLOv3: An Incremental Improvement. arXiv: 1804.02767, 2018; In press. doi: 10.48550/arXiv.1804.02767.
Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal speed and accuracy of object detection. arXiv, 2020; doi: 10.48550/arXiv.2004.10934.
He K, Kioxari G G, Dollar P, Girshick R. Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020; 42(2): 386–397.
Shao W, Kawakami R, Yoshihashi R, You S, Kawase H, Naemura T. Cattle detection and counting in UAV images based on convolutional neural networks. International Journal of Remote Sensing, 2020; 41(1): 31–52.
Weber F d L, Weber V A d M, Moraes P H d, Matsubara E T, Paiva D M B, Gomes M d N B, et al. Counting cattle in UAV images using convolutional neural networks. Remote Sensing Applications: Society and Environment, 2023; 29: 100900.
Andrew W, Gao J, Mullan S, Campbell N W, Dowsey A W, Burghardt T, et al. Visual identification of individual Hol-stein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture, 2021; 185: 106133.
Xu B, Wang W, Falzon G, Kwan P, Schneider D. Automated cattle counting using Mask R-CNN in quadcopter vision system. Computers and Electronics in Agriculture, 2020; 171: 105300.
Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA: IEEE, 2017; pp.726–727. doi: 10.1109/CVPR.2017.690.
Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017; 39(6): 1137–1149.
Soares V H A, Ponti M A, Gonçalves R A, Campello R J G B. Cattle counting in the wild with geolocated aerial images in large pasture areas. Computers and Electronics in Agriculture, 2021; 189: 106354.
Shang C, Wu F, Wang M L, Gao Q. Cattle behavior recognition based on feature fusion under a dual attention mechanism. Journal of Visual Communication and Image Representation, 2022; 85: 103524.
Wu D H, Wu Q, Yin X Q, Jiang B, Wang H, He D J, Song H b. Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector. Biosystems Engineering, 2020; 189: 150–163.
Beauchemin K A. Invited review: Current perspectives on eating and rumination activity in dairy cows. Journal of Dairy Science, 2018; 101: 4762–4784.
Wang Z, Meng F S, Liu S Q, Zhang Y, Zheng Z Q, Gong C L, et al. Cattle face recognition based on a Two-Branch convolutional neural network. Computers and Electronics in Agriculture, 2022; 196: 106871.
Shen W Z, Hu H Q, Dai B S, Wei X L, Sun J, et al. Individual identification of dairy cows based on convolutional neural networks. Multimedia Tools and Applications, 2020; 75: 14711–14724.
Gladstone J. YOLOv5: Better speed and accuracy. arXiv, 2021; In press
Zhang H Y, Wang Y, Dayoub F, Sunderhauf N. VarifocalNet: An IoU-aware dense object detector. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA: IEEE, 2020; pp.8510-8519.
Tong Z J, Chen Y H, Xu Z W, Yu R. Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. arXiv, 2023; In press.
Selvaraju R R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D, et al. Grad-CAM: Visual explanations from deep networks via Gradient-Based localization. IEEE International Conference on Computer Vision, 2019; 128: 336–359.
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, et al. SSD: Single shot MultiBox detector. In: Computer Vision - ECCV 2016, Springer, 2015. doi: 10.1007/978-3-319-46448-0_2
Copyright (c) 2025 International Journal of Agricultural and Biological Engineering

This work is licensed under a Creative Commons Attribution 4.0 International License.