| 基于机器视觉的红枣外部品质检测技术研究进展 |
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| 引用本文： | 刘鑫，马本学，李玉洁，等. 基于改进YOLOv7-ByteTrack的干制哈密大枣缺陷检测与计数系统[J]. 农业工程学报，2024，40(3)：303-312. DOI: 10.11975/j.issn.1002-6819.202310049 |
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| 作者姓名： | 刘鑫 马本学 李玉洁 陈金成 喻国威 |
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| 作者单位： | 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 |
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| 基金项目： | 国家自然科学基金项目（61763043）；2023年兵团研究生创新项目 |
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| 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、F1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。
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| 关 键 词： | 图像处理 目标检测 干制哈密大枣 多目标跟踪 YOLOv7 |
| 收稿时间： | 2023-10-10 |
| 修稿时间： | 2024-01-05 |
Research progress on external quality detection and classification technology of jujube based on machine vision |
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| LIU Xin, MA Benxue, LI Yujie, et al. Detecting and counting defects in dried Hami jujube using improved YOLOv7-ByteTrack[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(3): 303-312. DOI: 10.11975/j.issn.1002-6819.202310049 |
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| Authors: | LIU Xin MA Benxue LI Yujie CHEN Jincheng YU Guowei |
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| Affiliation: | 1.College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China;2.Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China;3.Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China;4.Mechanical Equipment Research Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China |
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| Abstract: | 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 count. Finally, the counting performance was achieved for dried Hami jujube. The software interface was designed using PyQt5 in the practical application. The model was then verified on the detection system of dried Hami jujube. The improved model was also deployed into the detection and counting system for online detection and counting of dried Hami jujube. The experimental results show that: 1) The floating-point computation of the improved YOLOv7 model was 64.6 G, which was 38.6% lower than that of the YOLOv7 model. The mAP, Recall, and F1-score on the test set reached 98.03%, 93.43%, and 95.00%, respectively, which increased by 4.40, 6.88, and 7.00 percent points, respectively, compared with the original. 2) The counting model with ByteTrack as the target detector showed an accuracy rate of 90.12% for the multi-category counting. The finding can also provide technical support for the detection, counting, sorting, and grading of dried Hami jujube. |
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| Keywords: | image processing object detection dried Hami jujubes multiple object tracking YOLOv7 |
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