{"id":"https://openalex.org/W4399144456","doi":"https://doi.org/10.1145/3654823.3654852","title":"ST-YOLO: An Improved Metal Defect Detection Model Based on Yolov5","display_name":"ST-YOLO: An Improved Metal Defect Detection Model Based on Yolov5","publication_year":2024,"publication_date":"2024-03-22","ids":{"openalex":"https://openalex.org/W4399144456","doi":"https://doi.org/10.1145/3654823.3654852"},"language":"en","primary_location":{"id":"doi:10.1145/3654823.3654852","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654823.3654852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning","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/A5114889274","display_name":"S. Sun","orcid":"https://orcid.org/0009-0004-7889-5809"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sijin Sun","raw_affiliation_strings":["Shanghai Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0004-7889-5809","affiliations":[{"raw_affiliation_string":"Shanghai Maritime University, China","institution_ids":["https://openalex.org/I96733725"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048065441","display_name":"Ming Deng","orcid":"https://orcid.org/0009-0007-2315-1468"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Deng","raw_affiliation_strings":["Shanghai Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0007-2315-1468","affiliations":[{"raw_affiliation_string":"Shanghai Maritime University, China","institution_ids":["https://openalex.org/I96733725"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102780048","display_name":"Jiawei Luo","orcid":"https://orcid.org/0009-0001-2457-2156"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Luo","raw_affiliation_strings":["Shanghai Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0001-2457-2156","affiliations":[{"raw_affiliation_string":"Shanghai Maritime University, China","institution_ids":["https://openalex.org/I96733725"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098933151","display_name":"Xiaohang Zheng","orcid":"https://orcid.org/0009-0009-1569-6307"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohang Zheng","raw_affiliation_strings":["Shanghai Maritime University, China"],"raw_orcid":"https://orcid.org/0009-0009-1569-6307","affiliations":[{"raw_affiliation_string":"Shanghai Maritime University, China","institution_ids":["https://openalex.org/I96733725"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5098933152","display_name":"Yang Pan","orcid":"https://orcid.org/0000-0002-4261-6829"},"institutions":[{"id":"https://openalex.org/I96733725","display_name":"Shanghai Maritime University","ror":"https://ror.org/04z7qrj66","country_code":"CN","type":"education","lineage":["https://openalex.org/I96733725"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Pan","raw_affiliation_strings":["Shanghai Maritime University, China"],"raw_orcid":"https://orcid.org/0000-0002-4261-6829","affiliations":[{"raw_affiliation_string":"Shanghai Maritime University, China","institution_ids":["https://openalex.org/I96733725"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5114889274"],"corresponding_institution_ids":["https://openalex.org/I96733725"],"apc_list":null,"apc_paid":null,"fwci":0.9992,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.77723249,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"158","last_page":"164"},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9868999719619751,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9757000207901001,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5126789808273315}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5126789808273315}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3654823.3654852","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3654823.3654852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8432136966","display_name":null,"funder_award_id":"X20231227","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2092072518","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W2997747012","https://openalex.org/W3012374719","https://openalex.org/W3122173535","https://openalex.org/W3138516171","https://openalex.org/W3209914922","https://openalex.org/W3210586215"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"In":[0,163],"industrial":[1,18],"applications,":[2],"surface":[3,25],"defects":[4,26],"in":[5,98,193,199],"metallic":[6],"materials":[7],"are":[8,174],"inevitable,":[9],"but":[10],"they":[11],"can":[12],"have":[13,53],"a":[14,29,190],"great":[15],"impact":[16],"on":[17,107,129,136],"reliability.":[19],"Moreover,":[20],"the":[21,54,77,91,143,148,152,156,182,187,200],"detection":[22,46,51],"of":[23,56,80,115,147,177,196],"metal":[24],"is":[27],"also":[28],"challenge":[30],"due":[31],"to":[32,63,84,94],"excessive":[33],"risks":[34],"caused":[35],"by":[36],"natural":[37],"corrosion,":[38],"manual":[39],"maintenance":[40],"costs,":[41],"and":[42,49,59,70,121,138,145,161,179],"harsh":[43],"environments.":[44],"Traditional":[45],"machine":[47],"learning":[48],"visual":[50],"methods":[52],"shortcomings":[55],"low":[57],"accuracy":[58,158],"poor":[60],"effect.":[61],"Due":[62],"limitations,":[64],"our":[65,166],"study":[66],"introduces":[67,101],"Swin":[68,74],"Transformer":[69,75],"Wise-IoU":[71,100],"Loss":[72],"methods.":[73],"leverages":[76],"pooling":[78],"technique":[79],"convolutional":[81],"neural":[82],"networks":[83],"facilitate":[85],"patch":[86],"merging,":[87],"merging":[88],"effect":[89],"improves":[90],"model\u2019s":[92,157],"ability":[93],"capture":[95],"multi-level":[96],"features":[97],"images.":[99],"an":[102],"attention":[103],"loss":[104],"function":[105,117],"based":[106,128],"bounding":[108],"box":[109],"regression":[110],"(BBR).":[111],"The":[112],"design":[113],"weights":[114],"this":[116],"exhibit":[118],"both":[119],"monotonic":[120],"dynamic":[122,126],"changes,":[123],"allowing":[124],"for":[125],"adjustment":[127],"real-time":[130],"training":[131],"conditions.":[132],"Experimental":[133],"results":[134,173],"conducted":[135],"NEU-DET":[137],"GC10-DET":[139],"data":[140],"sets":[141],"demonstrate":[142],"effectiveness":[144],"superiority":[146],"model.":[149,185],"By":[150],"using":[151],"[email":[153],"protected]":[154],"metric,":[155],"reaches":[159],"84.73%":[160],"67.85%.":[162],"comparative":[164],"experiments,":[165],"model":[167,188],"outperforms":[168],"other":[169],"control":[170],"models.":[171],"These":[172],"significant":[175],"improvements":[176],"9.6%":[178],"2.04%":[180],"over":[181],"baseline":[183],"Yolov5s":[184],"Furthermore,":[186],"played":[189],"positive":[191],"case":[192],"5":[194],"out":[195],"6":[197],"scenarios":[198],"ablation":[201],"experiments.":[202]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}