基于改进YOLOv5的牛个体图像识别方法

An Improved YOLOv5-based Method for Image Recognition of Cattle Individual

针对现有非接触式牛个体图像识别模型体积大、参数多、资源占用较大等问题,提出了一种基于改进YOLOv5模型的轻量级牛个体图像识别模型(Light YOLO Net,LY-Net)。将YOLOv5模型的主干网络替换为轻量级网络Ghost Net,并采用CARAFE(轻量级通用上采样算子),减少网络参数,实现网络轻量化;采用Focal-EIoU Loss作为损失函数,加速收敛并提高了速度。采用甘肃省张掖市某养殖场的30头牛,共6775幅牛个体图像作为样本数据,进行模型的训练、验证、测试。实验结果表明:LY-Net模型对牛个体的识别精确率约为99.6%,召回率约为99.5%。该模型能够在对牛个体图像高效且准确识别的同时,实现模型的小型化、轻量化。

Aiming at the current research problems of the existing non-contact cattle individual image recognition models,such as large volume,multiple parameters and large resource occupation,this paper proposes a lightweight cattle individual image recognition model(Light YOLO Net,LY-Net)based on improved YOLOv5 model.The backbone network of the YOLOv5 model is replaced by the lightweight network Ghost Net,and CARAFE(a lightweight universal up-sampling operator)is used to reduce network parameters and realize network lightweight.Focal-EIoU Loss is used as the loss function to accelerate the convergence and improve the speed.A total of 6775 individual cattle images of 30 cattle individuals on a farm in Zhangye City,Gansu Province are used as sample data for training,validation,and testing of the model.The experimental results show that the precision rate of LY-Net model for cattle individual recognition is about 99.6%,and the recall rate is about 99.5%.The proposed model can realize the miniaturization and lightweight of the model while effectively and accurately recognizing individual cattle images.