农业机械学报2024，Vol.55Issue(6)：237-245,9.DOI:10.6041/j.issn.1000-1298.2024.06.025
基于改进YOLO v5s模型的奶山羊乳房区域热红外图像检测方法
Thermal Infrared Image Detection Method of Dairy Goat Breast Region Based on Improved YOLO v5s Model
摘要
Abstract
Accurate extraction of the udder region of dairy goats was the key to realize non-invasive temperature detection of dairy goats.Due to the occlusion of breast area and the low quality of thermal infrared image,the detection accuracy needs to be further improved.Based on thermal infrared imaging technology,an improved YOLO v5s based detection method for key parts of milk goat udder was proposed.By replacing some convolutional modules of Backbone network in the original model with ShuffleNetV2 structure,the number of parameters in network deployment and training process was reduced,and the purpose of lightweight network design was realized.By introducing CBAM attention mechanism into the head of the Neck network detection head,the complexity of the network has been reduced and the detection accuracy of the breast region of dairy goats was ensured.Totally 4 611 infrared images of breast of pregnant dairy goats containing complete information,incomplete information and blurred edges were collected,and the model was trained after location labeling.After testing,the accuracy of the model was 93.7％,the recall rate was 86.1％,the mean average precision was 92.4％,the number of parameters was 8 × 105,and the floating point computation was 1.9 × 109.Compared with the YOLO v5n,YOLO v5s,YOLO v7-tiny,YOLO v7,YOLO v8n and YOLO v8s target detection network,the accuracy of this network was increased by 1.9 percentage points,1.2 percentage points,1.6 percentage points,4.3 percentage points,3.5 percentage points and 2.7 percentage points,the recall rate was increased by 3.4 percentage points,5.0 percentage points,0.1 percentage points,2.6 percentage points,0.9 percentage points and 1.5 percentage points,the number of parameters was decreased by 1.1 × 106,6.2 × 106,5.2 × 106,3.6 × 107,2.4 × 106 and 1.0 × 107,and floating-point calculations were reduced by 2.6 × 109,1.4 × 1010,1.1 × 1010,1.0 × 1011,6.8 × 109 and 2.7 × 1010,respectively.It met the detection requirements of the key parts of milk goat udder,and significantly reduced the number of parameters of the network without losing the detection accuracy,which was conducive to the deployment and use of the network in different environments,and had reference significance for the design of non-contact temperature monitoring system for milk goat body temperature.关键词
奶山羊/乳房/热红外图像/YOLO v5Key words
dairy goat/breast/thermal infrared image/YOLO v5分类
信息技术与安全科学引用本文复制引用
温毓晨,赵永杰,蒲六如,邓洪兴,张姝瑾,宋怀波..基于改进YOLO v5s模型的奶山羊乳房区域热红外图像检测方法[J].农业机械学报,2024,55(6):237-245,9.基金项目
国家重点研发计划项目(2023YFD1301800)和国家自然科学基金项目(32272931) （2023YFD1301800）
