Detection of attack behaviour of pig based on deep learning

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2023-09-21 DOI:10.1080/21642583.2023.2249934
Yanwen Li, Juxia Li, Tengxiao Na, Hua Yang
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Abstract

Attack behaviour detection of the pig is a valid method to protect the health of pig. Due to the farm conditions and the illumination changes of the piggery, the images of the pig in the videos are often being overlapped, which lead to difficulties in recognizing pig attack behaviour. We propose an improved YOLOX target detection model to overcome these difficulties. The improvements of the proposed model are: (1) the normalization attention mechanism is adopted to gain global information in the last block of the neck network and (2) the loss function IoU in YOLOX is replaced by DIoU to improve the detection accuracy. The pig attack behaviour considered in this paper includes the ear biting, the tail biting, the head to head collision and the head to body collision. The dataset is builded from the artificially observed attack video segments by using the inter-frame difference method. In the pig attack behaviour detection experiments, the improved YOLOX model achieves 93.21% precision which is 5.30% higher than the YOLOX model. The experiment results show that the improved YOLOX can realize pig attack behaviour detection with high precision.
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基于深度学习的猪攻击行为检测
猪的攻击行为检测是保护猪健康的有效方法。由于猪场条件和猪舍照明的变化,视频中猪的图像经常重叠,导致识别猪的攻击行为困难。我们提出了一种改进的YOLOX目标检测模型来克服这些困难。该模型的改进之处有:(1)采用归一化关注机制,在颈部网络的最后一块获取全局信息;(2)将YOLOX中的损失函数IoU替换为DIoU,提高检测精度。本文研究的猪的攻击行为包括咬耳朵、咬尾巴、头对头碰撞和头对身体碰撞。该数据集采用帧间差分法从人工观察到的攻击视频片段中构建而成。在猪攻击行为检测实验中,改进的YOLOX模型准确率达到93.21%,比YOLOX模型提高了5.30%。实验结果表明,改进后的YOLOX能够实现高精度的猪攻击行为检测。
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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