{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T02:49:43Z","timestamp":1781837383004,"version":"3.54.5"},"reference-count":25,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.<\/jats:p>","DOI":"10.3390\/s18113709","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T11:55:41Z","timestamp":1540986941000},"page":"3709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Limei","family":"Song","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyao","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0451-0377","authenticated-orcid":false,"given":"Xinjun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China"},{"name":"School of Electrical, Computer and Tele Communications Engineering, University of Wollongong, Wollongong, NSW 2500, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaidong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Precision Instrument, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1108\/03056120210407702","article-title":"Inspection of PCBs by laser-induced fluorescence","volume":"28","author":"Alaluf","year":"2013","journal-title":"Circuit World"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/j.rcim.2011.03.007","article-title":"Design and development of automatic visual inspection system for PCB manufacturing","volume":"27","author":"Mar","year":"2011","journal-title":"Robot. 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