山东农业大学学报（自然科学版）2025，Vol.56Issue(4)：616-627,12.DOI:10.3969/j.issn.1000-2324.2025.04.008
基于深度学习的家蚕计数与体长测量研究
Silkworm Counting and Body Length Measurement of Based Deep Learning
摘要
Abstract
Silkworm counting and body length measurement are essential processes in silkworm breeding.Traditional,they are mainly performed manually,which is easy to be influenced by subjective factors,and rather challenging to realize the rapid and accurate monitoring of silkworm counting and body length measurement.In this paper,we employ deep learning method to achieve silkworm counting and body length measurement.Taking feed-breeding silkworms as the research object,we construct a silkworm keypoint detection dataset and propose the YOLOv8-Pose-GE algorithm.This algorithm introduces GAM to the backbone of YOLOv8-Pose,amplifying global interactions,performing 3D alignment of multi-layer perceptrons,and enhancing the model's feature extraction capability while minimizing information loss.Additionally,adding ECA mechanism to the Neck part,which facilitates the aggregation of global spatial information and cross-channel interactions for modeling,improves the model's ability to perceive crucial features,thereby enable it to better process and extract silkworm keypoint features.The YOLOv8-Pose-GE achieves 94.7%mAP,95.31%Precision,and 87.98%Recall,outperforming existing keypoint detection methods.Moreover,the algorithm also balances speed,achieving an FPS of 37.61.Utilizing the coordinates output from the head part of YOLOv8-Pose-GE,this method can locate the position of the silkworms and their keypoints,connect the keypoints of the silkworms with straight lines in order,and obtain the body length of the silkworms from the length of the connecting lines,achieving silkworm counting simultaneously.In this paper,10 frames of randomly intercepted images from the video shooting of silkworms are counted for the experiment,and their mean absolute error(MAE_C),mean relative error(MRE_C)and mean squared deviation(MSD_C)are 1.6 individuals,3.6%and 2.1 individuals,respectively,which indicates that the model exhibits both high accuracy and stability.This study conducts measurement experiments on 40 silkworms(randomly selecting 8 from each of the instar stages1-5).The results show that the algorithm has the characteristic that the higher the silkworm instar,the better the measurement effect.Specifically for the 5th instar,the algorithm's MAE_L,MRE_L,MSD_L,and PCC in fifth instar are 12.29 px,1.87%,4.15px and 0.977,respectively,indicating a relatively small error.This method satisfies the needs of silkworm counting and body length measurement and can provide technical support to improve the quality of silkworm breeding and strengthen the selection and breeding of silkworm varieties.关键词
家蚕/深度学习/计数/体长/关键点检测Key words
Silkworm/deep learning/count/body length/keypoint detection分类
信息技术与安全科学引用本文复制引用
刘莫尘,孙崇凯,李正浩,常昊,尚明瑞,宋占华,刘贤军,孙廷举,闫银发..基于深度学习的家蚕计数与体长测量研究[J].山东农业大学学报（自然科学版）,2025,56(4):616-627,12.基金项目
山东省重点研发计划项目(2022TZXD0042) （2022TZXD0042）
国家蚕桑产业技术体系项目(CARS-18) （CARS-18）
山东省蚕桑产业技术体系建设项目(SDAT-18-06) （SDAT-18-06）
