{"title":"Latent Space Segmentation Model for Visual Surface Defect Inspection","authors":"Mingxu Li;Bo Peng;Donghai Zhai","doi":"10.1109/TIM.2024.3446650","DOIUrl":null,"url":null,"abstract":"There are a huge number of models that claim to enhance visual surface defect inspection accuracy. However, as these models generally function directly within the pixel space, optimizing advanced segmentation techniques frequently demands substantial computational resources and poses challenges for inference on devices with limited computing power. In addition, many current methodologies are deeply reliant on extensive surface defect datasets. In response to these challenges, our research presents a novel approach based on an auto-encoder structure that uses “latent space” to refine defect segmentation. Within the encoder segment of the autoencoder, we’ve incorporated contrastive learning, amplifying both feature extraction and segmentation capabilities. This architectural choice not only tailors the strategy for prompt response scenarios and underscores its precision in high-accuracy applications, but also addresses the challenges posed by the scarcity of defect samples. As a means to assess our approach and better cater to industrial applications that prioritize sample-level accuracy, we introduce innovative sample-level metrics, namely, mostly segmented (MS) and mostly lost (ML). Experiments conducted on the RSDD and Neuseg datasets underscore the strategy’s steadfast performance under diverse data circumstances. Synthesizing the benefits of latent space and contrastive learning, this article delineates proficient methodology for surface defect segmentation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10654578/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
There are a huge number of models that claim to enhance visual surface defect inspection accuracy. However, as these models generally function directly within the pixel space, optimizing advanced segmentation techniques frequently demands substantial computational resources and poses challenges for inference on devices with limited computing power. In addition, many current methodologies are deeply reliant on extensive surface defect datasets. In response to these challenges, our research presents a novel approach based on an auto-encoder structure that uses “latent space” to refine defect segmentation. Within the encoder segment of the autoencoder, we’ve incorporated contrastive learning, amplifying both feature extraction and segmentation capabilities. This architectural choice not only tailors the strategy for prompt response scenarios and underscores its precision in high-accuracy applications, but also addresses the challenges posed by the scarcity of defect samples. As a means to assess our approach and better cater to industrial applications that prioritize sample-level accuracy, we introduce innovative sample-level metrics, namely, mostly segmented (MS) and mostly lost (ML). Experiments conducted on the RSDD and Neuseg datasets underscore the strategy’s steadfast performance under diverse data circumstances. Synthesizing the benefits of latent space and contrastive learning, this article delineates proficient methodology for surface defect segmentation.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.