Detection of Cattle Key Parts Based on the Improved Yolov5 Algorithm

Dangguo Shao, Zihan He, Hongbo Fan () and Kun Sun
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Dangguo Shao: Faculty of Information Engineering and Automation, Yunnan Province Key Laboratory of Computer, Kunming University of Science and Technology, Kunming 650500, China
Zihan He: Faculty of Information Engineering and Automation, Yunnan Province Key Laboratory of Computer, Kunming University of Science and Technology, Kunming 650500, China
Hongbo Fan: Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650300, China
Kun Sun: Faculty of Information Engineering and Automation, Yunnan Province Key Laboratory of Computer, Kunming University of Science and Technology, Kunming 650500, China

Agriculture, 2023, vol. 13, issue 6, 1-16

Abstract: Accurate detection of key body parts of cattle is of great significance to Precision Livestock Farming (PLF), using artificial intelligence for video analysis. As the background image in cattle livestock farms is complex and the target features of the cattle are not obvious, traditional object-detection algorithms cannot detect the key parts of the image with high precision. This paper proposes the Filter_Attention attention mechanism to detect the key parts of cattle. Since the image is unstable during training and initialization, particle noise is generated in the feature graph after convolution calculation. Therefore, this paper proposes an attentional mechanism based on bilateral filtering to reduce this interference. We also designed a Pooling_Module, based on the soft pooling algorithm, which facilitates information loss relative to the initial activation graph compared to maximum pooling. Our data set contained 1723 images of cattle, in which labels of the body, head, legs, and tail were manually entered. This dataset was divided into a training set, verification set, and test set at a ratio of 7:2:1 for training the model proposed in this paper. The detection effect of our proposed module is proven by the ablation experiment from mAP, the AP value, and the F1 value. This paper also compares other mainstream object detection algorithms. The experimental results show that our model obtained 90.74% mAP, and the F1 value and AP value of the four parts were improved.

Keywords: key parts detection of cattle; Yolo; Filter_Attention; Softpooling; deep learning (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: 2023
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