基于改进YOLOv7-ByteTrack的干制哈密大枣缺陷检测与计数系统  

Detecting and counting defects in dried Hami jujube using improved YOLOv7-ByteTrack

作　　者：刘鑫 马本学[1,2,3] 李玉洁 陈金成 喻国威[1] LIU Xin;MA Benxue;LI Yujie;CHEN Jincheng;YU Guowei(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832003,China;Key Laboratory of Northwest Agricultural Equipment,Ministry of Agriculture and Rural Affairs,Shihezi 832003,China;Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery,Shihezi 832003,China;Mechanical Equipment Research Institute,Xinjiang Academy of Agricultural and Reclamation Science,Shihezi 832000,China)

机构地区：[1]石河子大学机械电气工程学院,石河子832003 [2]农业农村部西北农业装备重点实验室,石河子832003 [3]现代农业机械兵团重点实验室,石河子832003 [4]新疆农垦科学院机械装备研究所,石河子832000

出　　处：《农业工程学报》2024年第3期303-312,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61763043);2023年兵团研究生创新项目。

摘　　要：针对目前无法同时对随机多列排布干制哈密大枣进行快速缺陷检测和统计计数问题,该研究设计了一款干制哈密大枣在线检测与计数系统。以干制哈密大枣为研究对象,利用工业相机拍摄传送带上随机排列的多类别缺陷干制哈密大枣视频为数据源,采用改进的YOLOv7模型进行干制哈密大枣多类别缺陷检测并将检测结果作为后续多目标跟踪算法的输入;考虑到传送带上干制哈密大枣的外观相似性高以及排列密集等特点,该研究结合ByteTrack多目标跟踪算法的思想,设计了一种多类别干制哈密大枣的画线计数方法,实现了随机排布多类别干制哈密大枣的缺陷检测、准确定位及计数。试验结果表明:1)改进的YOLOv7模型浮点计算量为64.6 G,在干制哈密大枣目标检测数据的测试集上的平均检测精度、召回率、F_(1)平衡分数分别达到了98.03%、93.43%和95.00%,相比YOLOv7模型分别提高了4.40、6.88和7.00个百分点,浮点计算量下降了38.6%;2)基于改进YOLOv7为目标检测器开发的ByteTrack算法计数模型对干制哈密大枣计数的准确率为90.12%。该研究可为干制哈密大枣检测计数和分选分级提供技术支持。Defects are required to be removed in many rows of dried Hami jujubes.In this study,an online detection and counting system was designed for dried Hami jujubes that were randomly distributed on the conveyor belt.The system was divided into four parts,including a target detector,multi-target tracker,counter,and software interface.The target detector was used to detect the defect categories of dried Hami jujube and then mark the detection box.The multi-target tracker was used to distribute the identity information of dried Hami jujube in the video.The counter was used to count the number of dried Hami jujube in each defect category.The software interface was used to control the program execution.The accuracy of multi-target tracking depended mainly on target detection.The target detector was first designed to compare several models of YOLO series,in order to better detect the defects with high counting accuracy.The better detection of defects was achieved in the YOLOv7 model.After that,the 3×3 CBS convolution layer in the YOLOv7 model was replaced with DCNv2,DWconv,PConv,GSconv,and DSconv.A comparison was made to explore the influence of the improved model on the defect detection of dried Hami jujube.It was found that the DSconv convolution of the improved YOLOv7 model shared fewer parameters,the higher detection speed,and the better accuracy,compared with the original.Four attention mechanisms were compared,including CBAM,ECA,SE,and SimAM.ELAN and SPP layers were also added to the backbone network of the YOLOv7 model.SimAM was performed the best to increase the accuracy of target detection.The ByteTrack multi-target tracker was selected to deal with the high similarity in the different kinds of dried Hami jujubes under the actual environment.The reason was that the appearance information dominated the performance of the multi-target detector for data association.A line-drawing counting was also proposed for statistical counting.The coordinates and ID information were focused at both ends of the counting line to c

关 键 词：图像处理 目标检测 干制哈密大枣 多目标跟踪 YOLOv7 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]