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Oestrus detection in dairy cows by using atrous spatial pyramid and attention mechanism

文献类型： 外文期刊

作者： Wang, Rong 1 ; Bai, Qiang 1 ; Gao, Ronghua 2 ; Li, Qifeng 2 ; Zhao, Chunjiang 1 ; Li, Shuqin 1 ; Zhang, Hongming 1 ;

作者机构： 1.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Dairy cows oestrus; Mounting detection; YOLOv5; Multiscale optimisation; Loss function optimisation

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：5.002； 五年影响因子：5.321 ）

ISSN： 1537-5110

年卷期： 2022 年 223 卷

页码：

收录情况： SCI

摘要： The mounting behaviour of dairy cows is driven by oestrus and is also related to their welfare. To improve the accuracy and speed of the detection of mounting of dairy cows in dense scenes, a detection model for the mounting behaviour of dairy cows was developed in this study based on the feature enhancement module. Firstly, an improved attention module (C3GC-3) based on global context information and convolution was proposed to capture long-distance dependence. Then, a feature enhancement module based on atrous spatial pyramid pooling and the C3GC-3 was designed to realise the multiscale fusion of high-level semantic information and improve the feature extraction ability of the model for dense dairy cows images. Finally, the K-means clustering algorithm was used to obtain new anchors for the mounting behaviour of dairy cows, and CIoU was used to optimise the loss function. A camera was installed on a farm containing 200 dairy cows for data collection. The mosaic method was used to expand the 2668 images of the mounting behaviour of dairy cows to train the model, and the remaining 675 images were used to test the model. The experimental results showed that the proposed model had a high inference speed of 61 fps, and the detection time for each image was 16.3 ms, which met the real-time performance requirements for detecting mounting in dairy cows. The mean average precision of the model was 94.3%, which was 5.9% higher than that of YOLOv5l. The proposed model showed promising results regarding the mounting of dairy cows and might be used in any weather.(c) 2022 Published by Elsevier Ltd on behalf of IAgrE.

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