{"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/a-pcb-dataset-for-defects-detection-and","title":"A PCB Dataset for Defects Detection and Classification","arxiv_id":"1901.08204","date":"2019-01-24","proceeding":null,"authors":["Weibo Huang","Peng Wei"],"abstract":"To coupe with the difficulties in the process of inspection and\nclassification of defects in Printed Circuit Board (PCB), other researchers\nhave proposed many methods. However, few of them published their dataset\nbefore, which hindered the introduction and comparison of new methods. In this\npaper, we published a synthesized PCB dataset containing 1386 images with 6\nkinds of defects for the use of detection, classification and registration\ntasks. Besides, we proposed a reference based method to inspect and trained an\nend-to-end convolutional neural network to classify the defects. Unlike\nconventional approaches that require pixel-by-pixel processing, our method\nfirstly locate the defects and then classify them by neural networks, which\nshows superior performance on our dataset.","url_abs":"http://arxiv.org/abs/1901.08204v1","url_pdf":"http://arxiv.org/pdf/1901.08204v1.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":"a-pcb-dataset-for-defects-detection-and","repo_url":"https://github.com/Ironbrotherstyle/PCB-DATASET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-pcb-dataset-for-defects-detection-and","repo_url":"https://github.com/Ixiaohuihuihui/PCB-defect-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-pcb-dataset-for-defects-detection-and","repo_url":"https://github.com/MukundSai7907/PCB-Defects-Classification-Using-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-pcb-dataset-for-defects-detection-and","repo_url":"https://github.com/MukundSai7907/PCB-Defects-Detection-and-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-pcb-dataset-for-defects-detection-and","repo_url":"https://github.com/code-implementation1/Code6/tree/main/pcb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"pcb","method_name":"PCB"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1901.08204","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}