{"id":"https://openalex.org/W4378677045","doi":"https://doi.org/10.1145/3590003.3590079","title":"CBAM-based Method in YOLOv7 for Detecting Defective Vacuum Glass Tubes","display_name":"CBAM-based Method in YOLOv7 for Detecting Defective Vacuum Glass Tubes","publication_year":2023,"publication_date":"2023-03-17","ids":{"openalex":"https://openalex.org/W4378677045","doi":"https://doi.org/10.1145/3590003.3590079"},"language":"en","primary_location":{"id":"doi:10.1145/3590003.3590079","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3590003.3590079","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3590003.3590079","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3590003.3590079","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086649498","display_name":"Zhen-dong Sheng","orcid":"https://orcid.org/0009-0003-4205-4293"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyu Sheng","raw_affiliation_strings":["High School Affiliated to Fudan University, China"],"raw_orcid":"https://orcid.org/0009-0003-4205-4293","affiliations":[{"raw_affiliation_string":"High School Affiliated to Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034600001","display_name":"Haiguang Chen","orcid":"https://orcid.org/0009-0001-1322-4535"},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiguang Chen","raw_affiliation_strings":["Shanghai Normal University, China"],"raw_orcid":"https://orcid.org/0009-0001-1322-4535","affiliations":[{"raw_affiliation_string":"Shanghai Normal University, China","institution_ids":["https://openalex.org/I21945476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082511321","display_name":"Zifeng Qi","orcid":"https://orcid.org/0000-0001-6971-9601"},"institutions":[{"id":"https://openalex.org/I21945476","display_name":"Shanghai Normal University","ror":"https://ror.org/01cxqmw89","country_code":"CN","type":"education","lineage":["https://openalex.org/I21945476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zifeng Qi","raw_affiliation_strings":["Shanghai Normal University, China"],"raw_orcid":"https://orcid.org/0000-0001-6971-9601","affiliations":[{"raw_affiliation_string":"Shanghai Normal University, China","institution_ids":["https://openalex.org/I21945476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.048,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71468192,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"413","last_page":"418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.6317214369773865},{"id":"https://openalex.org/keywords/tube","display_name":"Tube (container)","score":0.600699245929718},{"id":"https://openalex.org/keywords/recall-rate","display_name":"Recall rate","score":0.5866532921791077},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5714048743247986},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.5272197723388672},{"id":"https://openalex.org/keywords/glass-tube","display_name":"Glass tube","score":0.4924703538417816},{"id":"https://openalex.org/keywords/vacuum-tube","display_name":"Vacuum tube","score":0.45498764514923096},{"id":"https://openalex.org/keywords/production-rate","display_name":"Production rate","score":0.4342747926712036},{"id":"https://openalex.org/keywords/process-engineering","display_name":"Process engineering","score":0.3703685998916626},{"id":"https://openalex.org/keywords/composite-material","display_name":"Composite material","score":0.33441367745399475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.30241847038269043},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15963107347488403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12331223487854004}],"concepts":[{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.6317214369773865},{"id":"https://openalex.org/C2777551473","wikidata":"https://www.wikidata.org/wiki/Q2093072","display_name":"Tube (container)","level":2,"score":0.600699245929718},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.5866532921791077},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5714048743247986},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.5272197723388672},{"id":"https://openalex.org/C2778334255","wikidata":"https://www.wikidata.org/wiki/Q2911301","display_name":"Glass tube","level":3,"score":0.4924703538417816},{"id":"https://openalex.org/C103753734","wikidata":"https://www.wikidata.org/wiki/Q3574371","display_name":"Vacuum tube","level":2,"score":0.45498764514923096},{"id":"https://openalex.org/C3020597237","wikidata":"https://www.wikidata.org/wiki/Q7798498","display_name":"Production rate","level":2,"score":0.4342747926712036},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.3703685998916626},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.33441367745399475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30241847038269043},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15963107347488403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12331223487854004},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3590003.3590079","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3590003.3590079","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3590003.3590079","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3590003.3590079","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3590003.3590079","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3590003.3590079","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378677045.pdf","grobid_xml":"https://content.openalex.org/works/W4378677045.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W234388709","https://openalex.org/W4205530313"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W2374179366","https://openalex.org/W2392305263"],"abstract_inverted_index":{"The":[0,73],"vacuum":[1],"glass":[2,44,110],"tube":[3],"is":[4,22],"one":[5],"of":[6,19,27,105,108],"the":[7,12,16,25,78,88,94,102],"most":[8],"important":[9],"materials":[10],"in":[11,42,61,93,112],"physical":[13],"industry,":[14],"and":[15,87],"inspection":[17,97],"rate":[18,80,90],"its":[20],"production":[21,26],"crucial":[23],"to":[24,39,52,65,68],"subsequent":[28],"products.":[29],"We":[30,56],"propose":[31],"a":[32],"CBAM-based":[33],"target":[34,71],"detection":[35,84],"method":[36],"for":[37,81],"YOLOv7":[38,62],"detect":[40],"defects":[41,107],"transparent":[43,54,109],"tubes,":[45],"which":[46],"are":[47],"not":[48],"easily":[49],"detectable":[50],"due":[51],"their":[53],"walls.":[55],"replace":[57],"all":[58],"pooling":[59],"layers":[60],"with":[63],"CBAM":[64],"enable":[66],"it":[67],"better":[69],"grasp":[70],"features.":[72],"experimental":[74],"results":[75],"show":[76],"that":[77],"recall":[79],"defective":[82],"product":[83],"reaches":[85,91],"98.34%":[86],"accuracy":[89,103],"96.33%":[92],"simulated":[95],"industrial":[96,113],"environment.":[98],"It":[99],"can":[100],"meet":[101],"requirements":[104],"detecting":[106],"tubes":[111],"sites.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
