{"id":"https://openalex.org/W4323344691","doi":"https://doi.org/10.1145/3568199.3568224","title":"Surface Defect Detection of Transparent Parts Based on Improved YOLOv4 Model","display_name":"Surface Defect Detection of Transparent Parts Based on Improved YOLOv4 Model","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4323344691","doi":"https://doi.org/10.1145/3568199.3568224"},"language":"en","primary_location":{"id":"doi:10.1145/3568199.3568224","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3568199.3568224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 5th International Conference on Machine Learning and Machine Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101984165","display_name":"Jing Wang","orcid":"https://orcid.org/0000-0002-9653-7253"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Wang","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, China"],"raw_orcid":"https://orcid.org/0000-0002-9653-7253","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087151182","display_name":"Jianhong Li","orcid":"https://orcid.org/0000-0002-3092-0452"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhong Li","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, China"],"raw_orcid":"https://orcid.org/0000-0002-3092-0452","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035435336","display_name":"Siwen Wei","orcid":"https://orcid.org/0000-0002-4525-4560"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siwen Wei","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, China"],"raw_orcid":"https://orcid.org/0000-0002-4525-4560","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1355,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.57508979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"154","last_page":"159"},"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":0.9988999962806702,"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":0.9988999962806702,"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":0.9876999855041504,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9781000018119812,"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/computer-science","display_name":"Computer science","score":0.7244274616241455},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5874751806259155},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5586087107658386},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4992563724517822},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4937230050563812},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48486098647117615},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4787237346172333},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.47794297337532043},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4407329857349396},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4386886656284332},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4356801509857178},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42694002389907837},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40655776858329773},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12800922989845276},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1110185980796814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7244274616241455},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5874751806259155},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5586087107658386},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4992563724517822},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4937230050563812},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48486098647117615},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4787237346172333},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.47794297337532043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4407329857349396},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4386886656284332},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4356801509857178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42694002389907837},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40655776858329773},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12800922989845276},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1110185980796814},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3568199.3568224","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3568199.3568224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 5th International Conference on Machine Learning and Machine Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6100000143051147,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2102605133","https://openalex.org/W2518103058","https://openalex.org/W2752782242","https://openalex.org/W2897689496","https://openalex.org/W2897772777","https://openalex.org/W2941001797","https://openalex.org/W2963037989","https://openalex.org/W2966926453","https://openalex.org/W3114104965","https://openalex.org/W3176659256"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4298130764","https://openalex.org/W4234808182","https://openalex.org/W2804364458","https://openalex.org/W2382043075","https://openalex.org/W2809151339","https://openalex.org/W2360673138","https://openalex.org/W2132641928","https://openalex.org/W4310225030"],"abstract_inverted_index":{"With":[0],"the":[1,25,33,56,72,82,90,95,107,112,116,121,141,147],"development":[2],"of":[3,28,36,58,94,115,132,140,149],"science":[4],"and":[5,15,135],"technology,":[6],"transparent":[7,29,40],"parts":[8],"are":[9],"almost":[10],"used":[11],"in":[12,50],"various":[13],"industries":[14],"play":[16],"an":[17,42,101],"indispensable":[18],"role.":[19],"It":[20],"is":[21,48,78,104,127],"meaningful":[22],"to":[23,54,80,88,106],"measure":[24],"surface":[26,37],"quality":[27],"parts.":[30],"To":[31],"improve":[32,89],"detection":[34,46],"accuracy":[35],"defects":[38],"on":[39,120],"parts,":[41],"improved":[43,117],"YOLOv4":[44,118,143],"target":[45],"model":[47,119],"proposed":[49],"this":[51],"paper.":[52],"Firstly,":[53],"expand":[55],"amount":[57],"dataset,":[59],"we":[60],"adopt":[61],"Mosaic":[62],"strategy":[63,67],"combined":[64],"with":[65,100],"Copy-Pasting":[66],"for":[68],"data":[69],"augmentation.":[70],"Secondly,":[71],"Canopy":[73],"+":[74],"K-means":[75],"clustering":[76],"algorithm":[77],"employed":[79],"select":[81],"initial":[83],"anchor":[84],"box":[85],"size.":[86],"Finally,":[87],"feature":[91],"representation":[92],"ability":[93],"model,":[96,144],"Squeeze-and-Excitation":[97],"Networks":[98],"(SENet)":[99],"attention":[102],"mechanism":[103],"embedded":[105],"model.":[108,151],"Experiments":[109],"show":[110],"that":[111,131,139],"mAP":[113],"value":[114],"test":[122],"set":[123],"reaches":[124],"91.07%,":[125],"which":[126,145],"6.4%":[128],"higher":[129,137],"than":[130,138],"Faster":[133],"R-CNN":[134],"4.15%":[136],"original":[142],"proves":[146],"effectiveness":[148],"our":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}