{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T17:12:06Z","timestamp":1732727526356,"version":"3.29.0"},"reference-count":19,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,10,14]]},"abstract":"In the production process of lithium battery, the quality inspection requirements of lithium battery are very high. At present, most of the work is done manually. Aiming at the problem of large manual inspection workload and large error, the robot visual inspection technology is applied to the production of lithium battery. In recent years, with the rapid development and progress of science and technology, the rapid development of visual detection hardware and algorithms, making it possible to screen defective products through visual detection algorithms. This paper takes lithium battery as the research object, and studies its vision detection algorithm. As a common commodity, the quality of lithium battery is the key for users to choose. With the increasing requirements of users for battery quality, how to produce high-quality battery is the key problem to be solved by manufacturers. However, at present, the defects of battery surface are mostly carried out manually. There are low efficiency and low detection rate in the process of manual detection. In this paper, the visual detection algorithm is studied to detect the defects such as pits, rust marks and broken skin on the surface of lithium battery, specifically to design the imaging experimental platform of lithium battery; use different lighting schemes to design different battery positioning and extraction algorithms; use Hough detection method to locate the battery surface, and design the battery defect algorithm for this, and compare the algorithm through experiments.<\/jats:p>","DOI":"10.3233\/jifs-189693","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T20:30:34Z","timestamp":1611347434000},"page":"4327-4335","source":"Crossref","is-referenced-by-count":0,"title":["Research on detection algorithm of lithium battery surface defects based on embedded machine vision"],"prefix":"10.1177","volume":"41","author":[{"given":"Yonggang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]},{"given":"Yufeng","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]},{"given":"Xiaomian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]},{"given":"Changwei","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]},{"given":"Shenyi","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]},{"given":"Xinyan","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]},{"given":"Zicong","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electrical Engineering, DongGuan Polytechnic, Dong Guan, China"},{"name":"Guangdong Textile Industry Intelligent Detection Engineering Technology Research Center (DGPT), Dong Guan, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189693_ref1","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/CVPR.2005.177","article-title":"Histograms of oriented gradients for human detection","volume":"1","author":"Dalal","year":"2005","journal-title":"2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"issue":"5","key":"10.3233\/JIFS-189693_ref5","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"141","author":"Krizhevsky","year":"2012","journal-title":"Neural Information Processing Systems"},{"key":"10.3233\/JIFS-189693_ref10","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.protcy.2015.02.008","article-title":"Improve the accuracy of crack length measurement using machine vision","volume":"19","author":"Kalili","year":"2015","journal-title":"Procedia Technology"},{"issue":"5","key":"10.3233\/JIFS-189693_ref12","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1007\/s00138-011-0403-3","article-title":"A Fast Regularity Measure for Surface Defect Detection","volume":"23","author":"Tasi","year":"2012","journal-title":"Machine Vision and Applications"},{"issue":"5","key":"10.3233\/JIFS-189693_ref13","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1109\/TCPMT.2011.2118208","article-title":"Feature-Extraction-Based Inspection Algorithm for IC Solder Joints","volume":"1","author":"Wu","year":"2011","journal-title":"IEEE Transactions on Components Packaging & Manufacturing Technology"},{"issue":"3","key":"10.3233\/JIFS-189693_ref14","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/TCPMT.2012.2231902","article-title":"Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine","volume":"3","author":"Wu","year":"2013","journal-title":"IEEE Transactions on Components Packaging & Manufacturing Technology"},{"issue":"2","key":"10.3233\/JIFS-189693_ref16","first-page":"103","article-title":"Development of an Automated Optical Inspection System for Mobile Phone Panels","volume":"34","author":"Torng","year":"2013","journal-title":"Journal of the Chinese Society of Mechanical Engineers"},{"issue":"12","key":"10.3233\/JIFS-189693_ref17","first-page":"2018","article-title":"Solder Joint Inspection Method for Chip Component Using Improved AdaBoost and Decision Tree","volume":"1","author":"Xie","year":"2012","journal-title":"IEEE Transactions on Components Packaging & Manufacturing Technology"},{"key":"10.3233\/JIFS-189693_ref19","unstructured":"Ioffe S. and Szegedy C. , Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning (2015), 448\u2013456."},{"issue":"1","key":"10.3233\/JIFS-189693_ref20","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research,"},{"issue":"2018","key":"10.3233\/JIFS-189693_ref21","first-page":"405","article-title":"Identification of structural dynamic characteristics based on machine vision technology","volume":"126","author":"Dong","journal-title":"Measurement,"},{"issue":"20","key":"10.3233\/JIFS-189693_ref22","doi-asserted-by":"crossref","first-page":"201104","DOI":"10.1063\/1.5094125","article-title":"RGB laser speckles based 3D profilometry","volume":"114","author":"Zhong","year":"2019","journal-title":"Applied Physics Letters"},{"issue":"6","key":"10.3233\/JIFS-189693_ref23","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/TPAMI.1983.4767447","article-title":"INSPECTOR: A Computer Vision System that Learns to Inspect Parts","volume":"PAMI-5","author":"Perkins","year":"1983","journal-title":"in IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/JIFS-189693_ref24","doi-asserted-by":"crossref","unstructured":"Pohle R. and Tnnies K.D. , A new approach for model-based adaptive region growing in medical image analysis, In: Proceeding of the 9th International Conference on Computer Analysis and Patterns Warsaw (2001), 238\u2013246.","DOI":"10.1007\/3-540-44692-3_30"},{"key":"10.3233\/JIFS-189693_ref25","unstructured":"Acker J. and Henrich D. , Manipulating deformable linear objects: Characteristic features for Vision-based detection of contact state transitions, In Proceedings of the IEEE International Symposium on Assembly and Task Planning (2003), 204\u2013209."},{"key":"10.3233\/JIFS-189693_ref27","doi-asserted-by":"crossref","unstructured":"Akeel H.A. and Holland S.W. , Product and Tehnology Trends for Industrial Robots, In IEEE Inter Conf On Robotics and Automation (2000), 696\u2013700.","DOI":"10.1109\/ROBOT.2000.844133"},{"key":"10.3233\/JIFS-189693_ref28","doi-asserted-by":"crossref","unstructured":"Schempf H. , Mutschler E. and Chemel B. , Evolution of Urban Robotic System Developments, In IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (1999), 19\u201323.","DOI":"10.1109\/AIM.1999.803251"},{"key":"10.3233\/JIFS-189693_ref29","doi-asserted-by":"crossref","first-page":"158","DOI":"10.4028\/www.scientific.net\/AMM.233.158","article-title":"Speed Servo Control of Pneumatic Manipulator Based on Sliding Mode Variable Structure Control","volume":"233","author":"Zeng","year":"2012","journal-title":"Applied Mechanics and Materials"},{"key":"10.3233\/JIFS-189693_ref31","doi-asserted-by":"crossref","unstructured":"Freund E. and Rokossa D. , Automatic Trajectory Generation for Multi-Joint Robot System, Industrial Electronics Society, Proceedings of the 24th Annual Conference of the IEEE, (8) (1998), 2216\u20132221.","DOI":"10.1109\/IECON.1998.724065"}],"container-title":["Journal of Intelligent & Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-189693","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T13:52:18Z","timestamp":1732629138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospress&doi=10.3233\/JIFS-189693"}},"subtitle":[],"editor":[{"given":"Shengzong","family":"Zhou","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,10,14]]},"references-count":19,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-189693","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"type":"print","value":"1064-1246"},{"type":"electronic","value":"1875-8967"}],"subject":[],"published":{"date-parts":[[2021,10,14]]}}}
