{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vision-based-inspection-system-employing","title":"Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components","arxiv_id":"1901.08864","date":"2019-01-25","proceeding":null,"authors":["Sarthak J. Shetty"],"abstract":"We are proceeding towards the age of automation and robotic integration of\nour production lines [5]. Effective quality-control systems have to be put in\nplace to maintain the quality of manufactured components. Among different\nquality-control systems, vision-based inspection systems have gained\nconsiderable amount of popularity [8] due to developments in computing power\nand image processing techniques. In this paper, we present a vision-based\ninspection system (VBI) as a quality-control system, which not only detects the\npresence of defects, such as in conventional VBIs, but also leverage\ndevelopments in machine learning to predict the presence of surface fractures\nand wearing. We use OpenCV, an open source computer-vision framework, and\nTensorflow, an open source machine-learning framework developed by Google Inc.,\nto accomplish the tasks of detection and prediction of presence of surface\ndefects such as fractures of manufactured gears.","url_abs":"http://arxiv.org/abs/1901.08864v1","url_pdf":"http://arxiv.org/pdf/1901.08864v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vision-based-inspection-system-employing","repo_url":"https://github.com/SarthakJShetty/Fracture","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"inception-v3","method_name":"Inception-v3"},{"method_slug":"inception-v3-module","method_name":"Inception-v3 Module"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}