农业机械学报2024，Vol.55Issue(3)：282-289,391,9.DOI:10.6041/j.issn.1000-1298.2024.03.028
基于YOLO v8n-seg-FCA-BiFPN的奶牛身体分割方法
Segmentation Model of Cow Body Parts Based on YOLO v8n-seg-FCA-BiFPN
摘要
Abstract
The fine segmentation of cow body parts has significant applications in research fields such as cow body condition scoring,posture estimation,behavior recognition,and body measurement.Due to the limited practicality of existing segmentation methods for different cow body parts,an improved YOLO v8n-seg model named YOLO v8n-seg-FCA-BiFPN was proposed for cow body part segmentation tasks.The improved model added FCA channel attention mechanism to the YOLO v8n backbone feature extraction network to better extract the geometric feature information of small targets,and used repeated weighted bidirectional features in the network feature fusion layer.The BiFPN was used to achieve the purpose of increasing the coupling of features at each scale.In order to validate the model performance,side-view images of cows at the channel were collected for network training.To ensure the quality of the dataset,the structural similarity algorithm was used to remove similar redundant images,resulting in a total of 1 452 images.LabelMe software was used to label the target cows,which were divided into eight parts,forelimbs,hindlimbs,udders,tails,belly,head,neck,and trunk,and was sent to the training model.The test results showed that the precision was 96.6％,the recall was 94.6％and the mean average precision was 97.1％,the parameters number was 3.3 × 106,and the detection speed was 6.2 f/s.The precision of each part was from 90.3％to 98.2％,and the mean average precision was 96.3％.The YOLO v8n-seg-FC A-BiFPN network could realize accurate segmentation of various parts of dairy cows.Compared with the original YOLO v8n,the precision,recall and mean average precision of YOLO v8n-seg-FCA-BiFPN were 3.2 percentages points,2.6 percentages points and 3.1 percentages points higher than that of YOLO v8n-seg,respectively.The precision under occlusion was 93.8％,the recall value was 91.67％,and the mean average precision was 93.15％.The volume of the improved model remained unchanged and had strong robustness.Under occlusion,the precision was 93.8％,the recall was 91.67％,and the mean average precision was 93.15％.The overall results showed that the research can provide necessary technical support for precise segmentation of dairy cows'body parts.关键词
奶牛/身体部位分割/语义分割/FCABasicBlock/BiFPN/YOLO v8nKey words
dairy cows/body part segmentation/semantic segmentation/FCABasicBlock/BiFPN/YOLO v8n分类
信息技术与安全科学引用本文复制引用
张姝瑾,许兴时,邓洪兴,温毓晨,宋怀波..基于YOLO v8n-seg-FCA-BiFPN的奶牛身体分割方法[J].农业机械学报,2024,55(3):282-289,391,9.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）
