基于改进YOLOv5s的猪脸识别检测方法  

Pig face recognition and detection method based on improved YOLOv5s

作　　者：李广博 查文文 陈成鹏 时国龙 辜丽川[1] 焦俊[1] LI Guang-bo;ZHA Wen-wen;CHEN Cheng-peng;SHI Guo-ong;GU Li-chuan;JIAO Jun(College of Information and Computer Science,Anhui Agricultural University,Hefei 230036,China)

机构地区：[1]安徽农业大学信息与计算机学院,合肥230036

出　　处：《西南农业学报》2023年第6期1346-1356,共11页Southwest China Journal of Agricultural Sciences

基　　金：国家自然科学基金项目(31671589);安徽省科技重大专项项目(201903a06020009,202103b06020013);安徽省教育厅自然基金项目(KJ2019A0209)。

摘　　要：[目的]针对传统生猪养殖耳标识别存在易脱落、易引起生猪感染等问题,采用改进YOLOv5s的模型对猪脸进行非入侵式识别。[方法]首先将K-Means的距离改为1-IOU,提高模型目标锚框的适应度;其次,引入CA坐标注意力机制,提高模型特征提取的能力;最后,引入BiFPN特征融合,有效利用特征提高模型的检测能力。试验采用的猪脸数据集共分为5类,数据增强后样本为12756张,训练集和测试集划分比例为9∶1。[结果]改进后的算法在准确率、召回率、平均精确率(IOU=0.5)分别达到0.926、0.897、0.955,比原始YOLOv5s算法分别提高13.2%、3.0%、2.2%,同时,改进后的算法在单只、多只、小目标、密集、有遮挡的场景下,泛化能力较强、识别精准度高。[结论]利用深度学习算法,可以获取生猪面部信息并准确识别,减少漏检、错检情况,为生猪智能化管理提供较好的技术支持。[Objective]Aiming at the problems of easy falling off and easy to cause pig infection in traditional pig breeding ear tag recognition,the paper used the improved YOLOv5s model to carry out non-invasive recognition of pig face.[Method]Firstly,the distance of K-Means was changed to 1-IOU,which improved the adaptability of the target anchor frame of the model;Secondly,the CA coordinate attention mechanism was introduced to improve the model feature extraction;Finally,BiFPN feature fusion was introduced to effectively use the features to improve the detection ability of the model.The pig face dataset used in the experiment was divided into 5 categories,with 12756 samples after data enhancement,and the training set and test set were divided into a ratio of 9∶1.[Result]The improved algorithm reached 0.926,0.897 and 0.955 in accuracy,recall,and average accuracy(IOU=0.5),which were 13.2%,3.0%and 2.2%,while the improved algorithm has better generalization ability and high recognition accuracy in single,multiple,small target,dense,and occluded scenes.[Conclusion]Using deep learning algorithm can get the facial information of pigs and identify them accurately,reduce the situation of missed inspection and wrong inspection,and provide better technical support for intelligent management of pigs.

关 键 词：猪脸识别 YOLOv5 K-MEANS 坐标注意力机制 BiFPN 

分 类 号：S2[农业科学—农业工程]