Cow-YOLO:Automatic cow mounting detection based on non-local CSPDarknet53 and multiscale neck  

作　　者：De Li Junhao Wang Zhe Zhang Baisheng Dai Kaixuan Zhao Weizheng Shen Yanling Yin Yang Li 

机构地区：[1]College of Electrical Engineering and Information,Northeast Agricultural University,Harbin 150030,China [2]College of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang 471023,Henan,China [3]College of Animal Sciences and Technology,Northeast Agriculture University,Harbin 150030,China [4]Key Laboratory of Northeast Smart Agricultural Technology of Ministry of Agriculture and Rural Affairs,Northeast Agricultural University,Harbin 150030,China

出　　处：《International Journal of Agricultural and Biological Engineering》2024年第3期193-202,共10页国际农业与生物工程学报（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.32072788,31902210,32002227,32172784);the National Key Research and Development Program of China(Grant No.2019YFE0125600);the earmarked fund(Grant No.CARS36).

摘　　要：Cows mounting behavior is a significant manifestation of estrus in cows.The timely detection of cows mounting behavior can make cows conceive in time,thereby improving milk production of cows and economic benefits of the pasture.Existing methods of mounting behavior detection are difficult to achieve precise detection under occlusion and severe scale change environments and meet real-time requirements.Therefore,this study proposed a Cow-YOLO model to detect cows mounting behavior.To meet the needs of real-time performance,YOLOv5s model is used as the baseline model.In order to solve the problem of difficult detection of cows mounting behavior in an occluded environment,the CSPDarknet53 of YOLOv5s is replaced with Non-local CSPDarknet53,which enables the network to obtain global information and improves the model’s ability to detect the mounting cows.Next,the neck of YOLOv5s is redesigned to Multiscale Neck,reinforcing the multi-scale feature fusion capability of model to solve difficulty detection under dramatic scale changes.Then,to further increase the detection accuracy,the Coordinate Attention Head is integrated into YOLOv5s.Finally,these improvements form a novel cow mounting detection model called Cow-YOLO and make Cow-YOLO more suitable for cows mounting behavior detection in occluded and drastic scale changes environments.Cow-YOLO achieved a precision of 99.7%,a recall of 99.5%,a mean average precision of 99.5%,and a detection speed of 156.3 f/s on the test set.Compared with existing detection methods of cows mounting behavior,Cow-YOLO achieved higher detection accuracy and faster detection speed in an occluded and drastic scale-change environment.Cow-YOLO can assist ranch breeders in achieving real-time monitoring of cows estrus,enhancing ranch economic efficiency.

关 键 词：cows mounting automatic detection Cow-YOLO computer vision CSPDarknet53 multiscale neck 

分 类 号：S237[农业科学—农业机械化工程] S823[农业科学—农业工程]