基于改进YOLOv4的输送带损伤检测方法

周宇杰; 徐善永; 黄友锐; 唐超礼

doi:10.13272/j.issn.1671-251x.17843

基于改进YOLOv4的输送带损伤检测方法

doi: 10.13272/j.issn.1671-251x.17843

安徽理工大学电气与信息工程学院, 安徽淮南 232001

基金项目:

安徽省教育厅自然科学研究重点项目（KJ2019A0110）。

详细信息

作者简介:
周宇杰(1996-),男,江苏宿迁人,硕士研究生,主要研究方向为图像处理技术,E-mail:1906173771@qq.com。

通讯作者:
徐善永(1983-),男,安徽固镇人,高级实验师,硕士研究生导师,主要研究方向为图像处理、机器人协同优化控制,E-mail:xsyong326@163.com。

中图分类号: TD528/634
计量
- 文章访问数: 176
- HTML全文浏览量: 26
- PDF下载量: 24
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-08
- 修回日期: 2021-11-05
- 刊出日期: 2021-11-20

Conveyor belt damage detection method based on improved YOLOv4

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 针对现有输送带损伤检测方法检测精度低、检测速度慢且缺少对面积较小损伤检测的问题，提出了一种基于改进YOLOv4的输送带损伤检测方法。该方法以YOLOv4为基础，对PANet路径融合网络部分进行改进，增加与浅层特征层的融合，将原3个尺度的特征层融合增加到4个尺度，提高模型对输送带损伤的特征提取能力，提高检测精度；将PANet部分每个特征层融合后的卷积次数由5次减少到3次，减少计算量，提高检测速度；对输送带损伤图像进行标注，并输入改进的YOLOv4模型进行训练和测试。实验结果表明，基于改进YOLOv4的输送带损伤检测方法损失收敛速度快，模型训练效果好；基于改进YOLOv4的输送带损伤检测方法对输送带撕裂、表面磨损和表面缺陷检测的平均精度均值达96.86%，检测速度达20.66帧/s，与YOLOv4，YOLOv3和Faster-RCNN相比，平均精度均值分别提升了1.4%，6.35%，2.16%，检测速度分别提升了2.39，2.34，15.25帧/s；与YOLOv4相比，基于改进YOLOv4的输送带损伤检测方法检测精度更高，对面积较小损伤的检测效果更好。
- 带式输送机 /
- 输送带损伤检测 /
- YOLOv4 /
- 深度学习 /
- PANet /
- 特征层融合
Abstract: In order to solve the problems of low detection precision, slow detection speed and lack of damage detection for small areas in existing conveyor belt damage detection methods, a conveyor belt damage detection method based on improved YOLOv4 is proposed.Based on YOLOv4, this method improves the PANet path fusion network part, increases the fusion with the shallow characteristic layer, increases the fusion of the original 3 scales of the characteristic layer to 4 scales, improves the characteristic extraction capability of the model for conveyor belt damage, and improves detection precision.The number of convolutions after fusion of each characteristic layer in the PANet part is reduced from 5 to 3 so as to reduce the amount of calculation and improve the detection speed.The conveyor belt damage images are labeled and input into the improved YOLOv4 model for training and testing.The experimental results show that the conveyor belt damage detection method based on improved YOLOv4 has a fast loss convergence speed and has a good model training effect.Based on improved YOLOv4 conveyor belt damage detection method, the average precision of the conveyor belt tear, surface wear and surface defect detection has reached 96.86%, and the detection speed has reached 20.66 frames/s.Compared with YOLOv4, YOLOv3 and Faster-RCNN, the average precision has increased by 1.4%, 6.35% and 2.16% respectively, and the detection speed has increased by 2.39, 2.34 and 15.25 frames/s respectively.Compared with YOLOv4, the conveyor belt damage detection method based on improved YOLOv4 has higher detection precision and better detection effect for small areas damages.
- belt conveyor /
- conveyor belt damage detection /
- YOLOv4 /
- deep learning /
- PANet /
- characteristic layer fusion

HTML全文

参考文献(15)

[1]	张春生.矿用带式输送机滚筒结构性能的研究[J].机械管理开发,2021,36(10):125-126. ZHANG Chunsheng.Structure and performance of mining belt conveyor drum in use[J].Mechanical Management and Development,2021,36(10):125-126.
[2]	张梦超,周满山,张媛,等.基于深度学习的矿用输送带损伤检测方法[J].工矿自动化,2021,47(6):51-56. ZHANG Mengchao,ZHOU Manshan,ZHANG Yuan,et al.Damage detection method for mine conveyor belt based on deep learning[J].Industry and Mine Automation,2021,47(6):51-56.
[3]	胡璟皓,高妍,张红娟,等.基于深度学习的带式输送机非煤异物识别方法[J].工矿自动化,2021,47(6):57-62. HU Jinghao,GAO Yan,ZHANG Hongjuan,et al.Research on the identification method of non-coal foreign object of belt conveyor based on deep learning[J].Industry and Mine Automation, 2021,47(6):57-62.
[4]	ALVIARI L P, ANGGAMAWARTI M F, SANJIWANI Y, et al.Classification of impact damage on a rubber-textile conveyor belt: a review[J].International Journal of Mechanical Engineering Technologies and Applications, 2020, 1(1): 21-27.
[5]	王以娜.基于视觉检测的皮带纵向撕裂检测关键技术研究[D].鞍山:辽宁科技大学,2020. WANG Yina.Research on key technology of belt longitudinal tear detection based on visual inspection[D].Anshan:University of Science and Technology Liaoning,2020.
[6]	武文泽.基于机器视觉的选煤厂输送机皮带磨损故障诊断研究[D].徐州:中国矿业大学,2020. WU Wenze.Research on fault diagnosis of conveyor belt wear in coal preparation plant based on machine vision[D].Xuzhou:China University of Mining and Technology,2020.
[7]	YANG Yanli, MIAO Changyun,LI Xianguo,et al.On-line conveyor belts inspection based on machine vision[J].Optik-International Journal for Light and Electron Optics,2014,125(19):5803-5807.
[8]	徐辉,刘丽静,沈科,等.基于多道线性激光的带式输送机纵向撕裂检测[J].工矿自动化,2021,47(7):37-44. XU Hui,LIU Lijing,SHEN Ke,et al.Longitudinal tear detection of belt conveyor based on multi linear lasers[J].Industry and Mine Automation, 2021,47(7):37-44.
[9]	YANG Yanli,ZHAO Yanfei,MIAO Changyun,et al.On-line longitudinal rip detection of conveyor belts based on machine vision[C]//IEEE International Conference on Signal and Image Processing(ICSIP), Beijing,2016:315-318.
[10]	LI Jie,MIAO Changyun.The conveyor belt longitudinal tear on-line detection based on improved SSR algorithm[J].Optik-International Journal for Light and Electron Optics,2016,127(19):8002-8010.
[11]	赵弼龙,乔铁柱.基于支持向量机红外图像分割的输送带纵向撕裂检测方法[J].工矿自动化,2014,40(5):30-33. ZHAO Bilong,QIAO Tiezhu.Detection method of belt longitudinal tear based on support vector machine and infrared image segmentation[J].Industry and Mine Automation,2014,40(5):30-33.
[12]	牛犇,乔铁柱,唐艳同.基于LabVIEW和CCD相机的输送带纵向撕裂检测系统设计[J].煤矿机械,2013,34(2):148-150. NIU Ben,QIAO Tiezhu,TANG Yantong.Detection system design for longitudinal tear of conveyor belt based on LabVIEW and CCD camera[J].Coal Mine Machinery,2013,34(2):148-150.
[13]	CHEN Xiaojiao,AN Zhenyu,HUANG Liansheng,et al.Surface defect detection of electric power equipment in substation based on improved YOLOV4 algorithm[C]//International Conference on Power and Energy Systems(ICPES),Chengdu,2020:256-261.
[14]	REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once: unified, real-ttime object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition,Las Vegas,2016:779-788.
[15]	HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(9):1904-1916.