建立基于YOLO目标检测的金属自行车车架缺陷动态检测系统

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971568

Su Kuan-Ying, Chen Ming-Fei, Tsai Po-Cheng, Tsai Cheng-Han

{"title":"建立基于YOLO目标检测的金属自行车车架缺陷动态检测系统","authors":"Su Kuan-Ying, Chen Ming-Fei, Tsai Po-Cheng, Tsai Cheng-Han","doi":"10.1109/IET-ICETA56553.2022.9971568","DOIUrl":null,"url":null,"abstract":"The purpose of this research is to develop a real-time bicycle frame's defect detection system using YOLO (You Only Look Once) and machine vision. Firstly, the defect locations are manually selected and a database is established. Next, a Darknet method is used to train the YOLO model. Its static detection accuracy rate is 92.6%, and then the static training model is combined with a robotic arm and an industrial camera to perform dynamic detection verification. The result shows that its detection rate reaches 87%. Finally, the above-mentioned defect detection technology is used with the detection machine to complete the development of the online defect detection system.","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Establish a Dynamic Detection System for Metal Bicycle Frame Defects Based on YOLO Object Detection\",\"authors\":\"Su Kuan-Ying, Chen Ming-Fei, Tsai Po-Cheng, Tsai Cheng-Han\",\"doi\":\"10.1109/IET-ICETA56553.2022.9971568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this research is to develop a real-time bicycle frame's defect detection system using YOLO (You Only Look Once) and machine vision. Firstly, the defect locations are manually selected and a database is established. Next, a Darknet method is used to train the YOLO model. Its static detection accuracy rate is 92.6%, and then the static training model is combined with a robotic arm and an industrial camera to perform dynamic detection verification. The result shows that its detection rate reaches 87%. Finally, the above-mentioned defect detection technology is used with the detection machine to complete the development of the online defect detection system.\",\"PeriodicalId\":46240,\"journal\":{\"name\":\"IET Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2022-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IET-ICETA56553.2022.9971568\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IET-ICETA56553.2022.9971568","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

本研究的目的是利用YOLO (You Only Look Once)和机器视觉技术开发一种自行车车架缺陷实时检测系统。首先，手工选择缺陷位置并建立缺陷数据库。其次，采用暗网方法对YOLO模型进行训练。其静态检测准确率为92.6%，然后将静态训练模型与机械臂和工业相机相结合，进行动态检测验证。结果表明，该方法的检出率可达87%。最后将上述缺陷检测技术与检测机结合使用，完成在线缺陷检测系统的开发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Establish a Dynamic Detection System for Metal Bicycle Frame Defects Based on YOLO Object Detection

The purpose of this research is to develop a real-time bicycle frame's defect detection system using YOLO (You Only Look Once) and machine vision. Firstly, the defect locations are manually selected and a database is established. Next, a Darknet method is used to train the YOLO model. Its static detection accuracy rate is 92.6%, and then the static training model is combined with a robotic arm and an industrial camera to perform dynamic detection verification. The result shows that its detection rate reaches 87%. Finally, the above-mentioned defect detection technology is used with the detection machine to complete the development of the online defect detection system.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.