Individual Recognition of a Group Beef Cattle Based on Improved YOLO v5

Ziruo Li, Yadan Zhang, Xi Kang, Tianci Mao, Yanbin Li and Gang Liu ()
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Ziruo Li: Key Lab of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China
Yadan Zhang: Key Lab of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China
Xi Kang: School of Computer and Data Engineering, NingboTech University, Ningbo 315100, China
Tianci Mao: Key Lab of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China
Yanbin Li: School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Gang Liu: Key Lab of Smart Agriculture Systems, Ministry of Education, China Agricultural University, Beijing 100083, China

Agriculture, 2025, vol. 15, issue 13, 1-22

Abstract: Deep learning-based individual recognition of beef cattle has improved the efficiency and effectiveness of individual recognition, providing technical support for modern large-scale farms. However, issues such as over-reliance on back patterns, similar patterns of adjacent cattle leading to low recognition accuracy, and difficulties in deploying models on edge devices exist in the process of group cattle recognition. In this study, we proposed a model based on improved YOLO v5. Specifically, a Simple, Parameter-Free (SimAM) attention module is connected with the residual network and Multidimensional Collaborative Attention mechanism (MCA) to obtain the MCA-SimAM-Resnet (MRS-ATT) module, enhancing the model’s feature extraction and expression capabilities. Then, the L M P D I o U loss function is used to improve the localization accuracy of bounding boxes during target detection. Finally, structural pruning is applied to the model to achieve a lightweight version of the improved YOLO v5. Using 211 test images, the improved YOLO v5 model achieved an individual recognition precision (P) of 93.2%, recall (R) of 94.6%, mean Average Precision (mAP) of 94.5%, FLOPs of 7.84, 13.22 M parameters, and an average inference speed of 0.0746 s. The improved YOLO v5 model can accurately and quickly identify individuals within groups of cattle, with fewer parameters, making it easy to deploy on edge devices, thereby accelerating the development of intelligent cattle farming.

Keywords: groups of beef cattle; individual recognition; deep learning; improved YOLO v5; structural pruning; target detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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