农业机械学报2024，Vol.55Issue(4)：231-240,10.DOI:10.6041/j.issn.1000-1298.2024.04.023
基于改进YOLO v5n的舍养绵羊行为识别方法
Behavior Recognition of Domesticated Sheep Based on Improved YOLO v5n
摘要
Abstract
Daily behavior is an important manifestation of the health status of livestock.In traditional behavior recognition methods,livestock usually need to be observed manually or rely on additional tools.In order to solve the above problems,an efficient sheep behavior recognition method was proposed based on the YOLO v5n model,which used the target recognition algorithm to recognize the feeding,lying and standing behaviors of domesticated sheep from the video sequence above the sheepflod.Firstly,the daily behavior images of sheep in the farm were collected by cameras,and the data set of sheep behavior was constructed.Secondly,SE attention mechanism was introduced into the Backbone feature extraction network of YOLO v5n to enhance the global information interaction and expression capability and improve the detection performance.The GIoU loss function was utilized to reduce the computational cost and improve the convergence speed of the model.Finally,GhostConv convolution was integrated into Backbone network,which effectively reduced the calculation and parameter number of the model.The experimental results showed that the parameter number of GS-YOLO v5n object detection method proposed was only 1.52 ×106,which was reduced by 15％compared with the original model YOLO v5n.The FLOPs was 3.3 × 109,which was 30％less than the original model.The average accuracy achieved 95.8％,which was 4.6 percentage points higher than that of the original model.Compared with the current mainstream YOLO series of object detection models,the improved model significantly reduced the computational and parameter complexity of the model,while also achieved higher detection accuracy.It was deployed on edge devices and met the standard of real-time detection.It can accurately and quickly locate and detect sheep,providing ideas and support for intelligent sheep breeding.关键词
舍养绵羊/智慧养殖/行为识别/注意力机制/YOLO v5n/绵羊数据集Key words
domesticated sheep/intelligent farming/behavior recognition/attention mechanism/YOLO v5n/sheep data分类
信息技术与安全科学引用本文复制引用
翟亚红,王杰,徐龙艳,祝岚,原红光,赵逸凡..基于改进YOLO v5n的舍养绵羊行为识别方法[J].农业机械学报,2024,55(4):231-240,10.基金项目
湖北省教育厅重点科研项目(D20211802)和湖北省科技厅重点研发计划项目(2022BEC008) （D20211802）
