Mengran Zhou , Yue Chen , Feng Hu , Wenhao Lai , Lipeng Gao
{"title":"基于多光谱成像的输送带磨损状态精确评估深度学习方法","authors":"Mengran Zhou , Yue Chen , Feng Hu , Wenhao Lai , Lipeng Gao","doi":"10.1016/j.optlastec.2024.111782","DOIUrl":null,"url":null,"abstract":"<div><p>Accurate assessment of the conveyor belt wear state is a crucial part of measuring belt conveyor safety and reliability. Therefore, this paper proposes an accurate detection approach for conveyor belt wear based on multispectral imaging(MSI), and designs a lightweight network model, named depthwise shuffle coordinate attention network (DSCANet) to assess and classify conveyor belts in three wear states. The multispectral images of the conveyor belt in the Huainan mining area were collected by the MSI system, with a wavelength range of 675–975 nm. The multispectral data at the wavelength with the largest imaging differences was screened as the input to the assessment model DSCANet. Compared with other widely used neural network models, the proposed DSCANet demonstrated the best performance, achieving a classification accuracy of 98.78 %, with floating point operations(FLOPs) of only 136.53M. The findings indicate the great efficacy of the MSI and DSCANet combination in assessing the conveyor belt wear, holding importance in reducing the risk of sudden failures and enhancing production efficiency.</p></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"181 ","pages":"Article 111782"},"PeriodicalIF":4.6000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep learning approach for accurate assessment of conveyor belt wear state based on multispectral imaging\",\"authors\":\"Mengran Zhou , Yue Chen , Feng Hu , Wenhao Lai , Lipeng Gao\",\"doi\":\"10.1016/j.optlastec.2024.111782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Accurate assessment of the conveyor belt wear state is a crucial part of measuring belt conveyor safety and reliability. Therefore, this paper proposes an accurate detection approach for conveyor belt wear based on multispectral imaging(MSI), and designs a lightweight network model, named depthwise shuffle coordinate attention network (DSCANet) to assess and classify conveyor belts in three wear states. The multispectral images of the conveyor belt in the Huainan mining area were collected by the MSI system, with a wavelength range of 675–975 nm. The multispectral data at the wavelength with the largest imaging differences was screened as the input to the assessment model DSCANet. Compared with other widely used neural network models, the proposed DSCANet demonstrated the best performance, achieving a classification accuracy of 98.78 %, with floating point operations(FLOPs) of only 136.53M. The findings indicate the great efficacy of the MSI and DSCANet combination in assessing the conveyor belt wear, holding importance in reducing the risk of sudden failures and enhancing production efficiency.</p></div>\",\"PeriodicalId\":19511,\"journal\":{\"name\":\"Optics and Laser Technology\",\"volume\":\"181 \",\"pages\":\"Article 111782\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics and Laser Technology\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0030399224012404\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics and Laser Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030399224012404","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
A deep learning approach for accurate assessment of conveyor belt wear state based on multispectral imaging
Accurate assessment of the conveyor belt wear state is a crucial part of measuring belt conveyor safety and reliability. Therefore, this paper proposes an accurate detection approach for conveyor belt wear based on multispectral imaging(MSI), and designs a lightweight network model, named depthwise shuffle coordinate attention network (DSCANet) to assess and classify conveyor belts in three wear states. The multispectral images of the conveyor belt in the Huainan mining area were collected by the MSI system, with a wavelength range of 675–975 nm. The multispectral data at the wavelength with the largest imaging differences was screened as the input to the assessment model DSCANet. Compared with other widely used neural network models, the proposed DSCANet demonstrated the best performance, achieving a classification accuracy of 98.78 %, with floating point operations(FLOPs) of only 136.53M. The findings indicate the great efficacy of the MSI and DSCANet combination in assessing the conveyor belt wear, holding importance in reducing the risk of sudden failures and enhancing production efficiency.
期刊介绍:
Optics & Laser Technology aims to provide a vehicle for the publication of a broad range of high quality research and review papers in those fields of scientific and engineering research appertaining to the development and application of the technology of optics and lasers. Papers describing original work in these areas are submitted to rigorous refereeing prior to acceptance for publication.
The scope of Optics & Laser Technology encompasses, but is not restricted to, the following areas:
•development in all types of lasers
•developments in optoelectronic devices and photonics
•developments in new photonics and optical concepts
•developments in conventional optics, optical instruments and components
•techniques of optical metrology, including interferometry and optical fibre sensors
•LIDAR and other non-contact optical measurement techniques, including optical methods in heat and fluid flow
•applications of lasers to materials processing, optical NDT display (including holography) and optical communication
•research and development in the field of laser safety including studies of hazards resulting from the applications of lasers (laser safety, hazards of laser fume)
•developments in optical computing and optical information processing
•developments in new optical materials
•developments in new optical characterization methods and techniques
•developments in quantum optics
•developments in light assisted micro and nanofabrication methods and techniques
•developments in nanophotonics and biophotonics
•developments in imaging processing and systems