{"id":"https://openalex.org/W4393285764","doi":"https://doi.org/10.1109/tim.2024.3382737","title":"Dynamic Reasoning Network for Image-Level Supervised Segmentation on Metal Surface Defect","display_name":"Dynamic Reasoning Network for Image-Level Supervised Segmentation on Metal Surface Defect","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4393285764","doi":"https://doi.org/10.1109/tim.2024.3382737"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2024.3382737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3382737","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5060088885","display_name":"Ruiyun Yu","orcid":"https://orcid.org/0000-0003-0523-6242"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiyun Yu","raw_affiliation_strings":["School of Software Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0003-0523-6242","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047034942","display_name":"Bingyang Guo","orcid":"https://orcid.org/0000-0002-6630-0044"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingyang Guo","raw_affiliation_strings":["School of Software Engineering, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-6630-0044","affiliations":[{"raw_affiliation_string":"School of Software Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":1.1257,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.79097599,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"10"},"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":0.9897000193595886,"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":0.9897000193595886,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.7215564250946045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6767815947532654},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6101953387260437},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5943930745124817},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.5267127156257629},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49434876441955566},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49251994490623474},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47782424092292786},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4518147110939026},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.43084716796875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1950817108154297}],"concepts":[{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.7215564250946045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6767815947532654},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6101953387260437},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5943930745124817},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.5267127156257629},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49434876441955566},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49251994490623474},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47782424092292786},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4518147110939026},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.43084716796875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1950817108154297},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2024.3382737","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3382737","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3900590269","display_name":"\u57fa\u4e8e\u6570\u5b57\u5b6a\u751f\u548cAIOT\u7684\u667a\u6167\u6d88\u9632\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"62072094","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4933828724","display_name":null,"funder_award_id":"XLYC2005001","funder_id":"https://openalex.org/F4320329895","funder_display_name":"Liaoning Revitalization Talents Program"},{"id":"https://openalex.org/G684547677","display_name":null,"funder_award_id":"N2317001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329895","display_name":"Liaoning Revitalization Talents Program","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2295107390","https://openalex.org/W2747355315","https://openalex.org/W2765793020","https://openalex.org/W2787091153","https://openalex.org/W2798376494","https://openalex.org/W2799124825","https://openalex.org/W2962758679","https://openalex.org/W2962858109","https://openalex.org/W2966401131","https://openalex.org/W2970822152","https://openalex.org/W2983660929","https://openalex.org/W2985459778","https://openalex.org/W2994615081","https://openalex.org/W2997851315","https://openalex.org/W3034930876","https://openalex.org/W3113270537","https://openalex.org/W3129082918","https://openalex.org/W3159892859","https://openalex.org/W3164289800","https://openalex.org/W3165000817","https://openalex.org/W3173957243","https://openalex.org/W3176482836","https://openalex.org/W3192423452","https://openalex.org/W3194222902","https://openalex.org/W3196806685","https://openalex.org/W4206227497","https://openalex.org/W4225898116","https://openalex.org/W4226158211","https://openalex.org/W4226320329","https://openalex.org/W4285120968","https://openalex.org/W4287887367","https://openalex.org/W4312647976","https://openalex.org/W4312680544","https://openalex.org/W4313270795","https://openalex.org/W4313327864","https://openalex.org/W4317038476","https://openalex.org/W4321380746","https://openalex.org/W4367665525","https://openalex.org/W4382699971","https://openalex.org/W4385786080","https://openalex.org/W4386071966","https://openalex.org/W4387469824","https://openalex.org/W4390871803","https://openalex.org/W6789782624"],"related_works":["https://openalex.org/W1986655823","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W3011384228","https://openalex.org/W2945274617","https://openalex.org/W3199300986","https://openalex.org/W4313052709","https://openalex.org/W4298131179","https://openalex.org/W2375430703"],"abstract_inverted_index":{"Segmenting":[0],"defects":[1,60,112],"on":[2,19,24,206],"metal":[3,58,208],"surfaces":[4],"is":[5,127,142],"crucial":[6],"for":[7,27,110,129],"assuring":[8],"product":[9],"quality":[10],"in":[11,31,46],"industrial":[12],"manufacturing.":[13],"Current":[14],"defect":[15,210],"segmentation":[16],"solutions":[17],"based":[18],"deep":[20],"learning":[21],"heavily":[22],"depend":[23],"extensive":[25],"annotations":[26],"model":[28],"training,":[29],"resulting":[30],"a":[32,51,92,121,155,167],"time-consuming":[33],"and":[34,79,101,113,117,136,149,159,170,177,195],"labor-intensive":[35],"process.":[36],"In":[37],"order":[38],"to":[39,56,75,95,144,162,186],"alleviate":[40,145],"the":[41,63,67,76,97,131,146,164,172,175,178,183,200],"burden":[42],"of":[43,99,133,166,190],"accurate":[44,139],"annotation":[45],"pixel-level,":[47],"this":[48],"paper":[49],"proposes":[50],"novel":[52,122],"weakly":[53],"supervised":[54],"network":[55,68],"segment":[57],"surface":[59,209],"only":[61],"using":[62],"image-level":[64],"label.":[65],"Specifically,":[66],"input":[69],"comprises":[70],"two":[71],"defective":[72],"images":[73,88],"belonging":[74],"same":[77],"category":[78,169],"one":[80],"normal":[81,116],"image":[82],"without":[83],"any":[84],"defects.":[85,191],"The":[86],"triplet":[87,160],"are":[89],"put":[90],"into":[91],"weight-shared":[93],"backbone":[94],"extract":[96],"representation":[98],"foreground":[100,176],"background.":[102,179],"This":[103],"combination":[104],"can":[105],"efficiently":[106],"offer":[107],"similarity":[108],"data":[109],"identical":[111],"distinguish":[114],"between":[115,174],"abnormal":[118],"samples.":[119],"Then,":[120],"attention":[123],"activation":[124],"aggregation":[125],"module":[126,158],"responsible":[128],"combining":[130],"features":[132],"different":[134,187],"levels":[135],"obtaining":[137],"more":[138],"maps,":[140],"which":[141],"beneficial":[143],"false":[147],"positive":[148],"negative":[150],"predictions.":[151],"Finally,":[152],"we":[153],"design":[154],"dynamic":[156],"reasoning":[157],"loss":[161],"explore":[163],"relevance":[165],"common":[168],"identify":[171],"difference":[173],"It":[180],"also":[181],"improves":[182],"model\u2019s":[184],"generalization":[185],"morphological":[188],"changes":[189],"Extensive":[192],"comparison":[193],"experiments":[194],"ablation":[196],"studies":[197],"prove":[198],"that":[199],"proposed":[201],"method":[202],"achieves":[203],"state-of-the-art":[204],"performance":[205],"six":[207],"datasets.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}