High-Accuracy Detection and Classification of Defect and Deformation of Metal Screw Head Achieved by Convolutional Neural Networks

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Zhang, Y. Zhang, Design of product defects detect system based on machine vision, Ind. Control Comput. 27 (2014) 58-59." /> <meta name="citation_reference" content="Y. Chen, X. Zhu, Y. Wang, J. Gu, L. Chen, Research on image recognition of internal thread, Tool Eng. 48 (2014) 77-79." /> <meta name="citation_reference" content="R. Yan, X. Chen, J. Wang, X. 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However, the products must also maintain high quality and reliability. An efficient inspection technique should be conducted for the improvement in the manufacturing quality. In order to achieve high inspection rate, optical inspection based on machine vision often raises the threshold of the judgment and this will worsen false detection. In this study, we propose a high-accuracy optical inspection system based on deep learning technology. Various defects in screw head are precisely detected and analyzed, which include surface damage, unprocessed, and stripped surfaces. An industrial camera and microscope system are employed to collect the raw images of metal screws with different defect types. The raw images of 3200 are utilized to train the designed convolutional neural networks. The experimental results indicate that the proposed system reaches a detection accuracy of 92.8% and the average detection speed is 0.03 second per image. In comparison with conventional machine vision methods, the proposed measurement system is more suitable for the inspection of industrial production line." /> <meta name="keywords" content="Convolutional Neural Network, Defect Detection, Screw Head" /> <meta name="copyright" content="2022 Trans Tech Publications Ltd. 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Achieved by Convolutional Neural Networks</h1> </div> <div class="paper-statistics"> <div class="loading"> <i class="inline-icon download-and-visitor-statistics-icon"></i> <span class="normal-text" id="paperDownloadsAndVisitorsCount"></span> </div> </div> <div class="clearfix"></div> <div class="page-paper-title"> <div class="preview-block"> <img alt="Article Preview" width="128" height="180" src="/AMM.909.75/thumbnail.gif"> <div id="preview-button" data-url-preview-log="/Paper/PreviewImageLog?paperId=590495"> <i class="inline-icon preview-icon"></i> </div>  <div id="paper-preview-modal" class="modal fade"> <div class="modal-dialog" role="document"> <div class="popup-page-name underline-begin"> <div class="page-name-block-text">Article Preview</div> </div> <img alt="Article Preview" class="preview-image lazyload" data-src="/AMM.909.75/preview.gif"> <a data-dismiss="modal" title="Close" class="inline-icon close-icon"></a> </div> </div>  </div> <div class="abstract-block-description"> <h3 class="page-paper-first-header">Abstract:</h3> <p class="normal-text"> Nowadays, the industry requires automatic production for high-speed manufacturing. However, the products must also maintain high quality and reliability. An efficient inspection technique should be conducted for the improvement in the manufacturing quality. In order to achieve high inspection rate, optical inspection based on machine vision often raises the threshold of the judgment and this will worsen false detection. In this study, we propose a high-accuracy optical inspection system based on deep learning technology. Various defects in screw head are precisely detected and analyzed, which include surface damage, unprocessed, and stripped surfaces. An industrial camera and microscope system are employed to collect the raw images of metal screws with different defect types. The raw images of 3200 are utilized to train the designed convolutional neural networks. The experimental results indicate that the proposed system reaches a detection accuracy of 92.8% and the average detection speed is 0.03 second per image. In comparison with conventional machine vision methods, the proposed measurement system is more suitable for the inspection of industrial production line. </p> </div> <div class="paper-access-buttons col-xs-12"> <div class="row"> <div class="sa-button-wrap"> <a id="sa-button" class="wayfinder-login d-flex sa-button" href="javascript:;"> <div class="sa-button-logo-wrap"> <i class="inline-icon sa-white"></i> </div> <div class="d-flex justify-content-center align-items-center sa-button-text text-truncate"> <div class="sa-button-text-primary text-truncate">Access through your institution</div> </div> </a> </div> <div class="title-button-pdf"> <button id="readPaperButton" data-url-read-paper-log="/Paper/ReadThePaperLog?paperId=590495" class="button button-160"> <span class="inline-element">Read The Paper</span> </button> </div> </div> <div class="row"> </div> </div> <div class="clearfix"></div> <div class="connected-title-container"> <div class="connected-title-text 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