农业工程学报2023，Vol.39Issue(15)：152-162,11.DOI:10.11975/j.issn.1002-6819.202303122
融合YOLOv5s与通道剪枝算法的奶牛轻量化个体识别方法
Light-weight recognition network for dairy cows based on the fusion of YOLOv5s and channel pruning algorithm
摘要
Abstract
Real-time and accurate individual identification of dairy cows is a prerequisite for building a perfect technical architecture for precision dairy farming.It is crucial to ensure that the identification model is lightweight while identifying individual cows quickly and accurately.In this research,a fast and accurate identification model of individual cows with low computation and small number of parameters was proposed.YOLOv5s network was selected as the original model.The scale factor in the batch normalization layer was used as the basis for judging the importance of the channel in the model for reducing the network complexity.In order to compress the model effectively,sparse loss term was added to the loss function to sparse model channels.Experimental results demonstrate that the mAP of the pruned model was 99.50%,the floating point operations(FLOPs)was 8.1 G,the number of parameters(Params)was 1.630 M,and the detection speed was 135.14 frames/s.Among all the similar methods which have been compared,the proposed method has the smallest model complexity.Moreover,the proposed model was less dependent on coat patterns and had better performance under low illumination conditions than other models in robustness.The proposed method has the characteristics of fast,accurate,robust,low computational cost and small number of parameters.It is of great potential in advancing the refinement of cow breeding on farm management.关键词
图像识别/动物/奶牛/轻量化/身份识别/通道剪枝Key words
image recognition/animals/dairy cows/light-weight/identification/channel prune分类
计算机与自动化引用本文复制引用
许兴时,王云飞,华志新,杨广元,李慧敏,宋怀波..融合YOLOv5s与通道剪枝算法的奶牛轻量化个体识别方法[J].农业工程学报,2023,39(15):152-162,11.基金项目
National Natural Science Foundation of China(32272931) （32272931）
Shaanxi Provincial Technology Innovation Guidance Planned Program(2022QFY11-02) （2022QFY11-02）
