{"id":"https://openalex.org/W4401943610","doi":"https://doi.org/10.1109/tim.2024.3446650","title":"Latent Space Segmentation Model for Visual Surface Defect Inspection","display_name":"Latent Space Segmentation Model for Visual Surface Defect Inspection","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401943610","doi":"https://doi.org/10.1109/tim.2024.3446650"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2024.3446650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3446650","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/A5103234130","display_name":"Mingxu Li","orcid":"https://orcid.org/0000-0001-9198-4364"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingxu Li","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010159266","display_name":"Bo Peng","orcid":"https://orcid.org/0000-0002-8694-5106"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Peng","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009537215","display_name":"Donghai Zhai","orcid":"https://orcid.org/0000-0001-8396-5710"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Donghai Zhai","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China"],"affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103234130"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":1.1441,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.80254491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"11"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9958000183105469,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.575244128704071},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5648233890533447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5353472828865051},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.512428343296051},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5001716613769531},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4942210018634796},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.488753080368042},{"id":"https://openalex.org/keywords/visual-inspection","display_name":"Visual inspection","score":0.4172508418560028},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34492382407188416},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3373497724533081},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15337449312210083},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.14327359199523926}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.575244128704071},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5648233890533447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353472828865051},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.512428343296051},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5001716613769531},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4942210018634796},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.488753080368042},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.4172508418560028},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34492382407188416},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3373497724533081},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15337449312210083},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.14327359199523926},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2024.3446650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3446650","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/G4454505756","display_name":null,"funder_award_id":"202307000076","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G5173025782","display_name":null,"funder_award_id":"2024ZDZX0001","funder_id":"https://openalex.org/F4320336756","funder_display_name":"Tianjin Science and Technology Program"},{"id":"https://openalex.org/G5589138753","display_name":null,"funder_award_id":"61961038","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320336756","display_name":"Tianjin Science and Technology Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2129440600","https://openalex.org/W2135460975","https://openalex.org/W2149032670","https://openalex.org/W2183790810","https://openalex.org/W2736973763","https://openalex.org/W2746791238","https://openalex.org/W2761216034","https://openalex.org/W2787091153","https://openalex.org/W2917344403","https://openalex.org/W2922706279","https://openalex.org/W2945270739","https://openalex.org/W2962785568","https://openalex.org/W2998291476","https://openalex.org/W3008381189","https://openalex.org/W3009635072","https://openalex.org/W3023371261","https://openalex.org/W3093222659","https://openalex.org/W3094374092","https://openalex.org/W3100487172","https://openalex.org/W3106583357","https://openalex.org/W3122412340","https://openalex.org/W3152567630","https://openalex.org/W3164289800","https://openalex.org/W3164791059","https://openalex.org/W3170841864","https://openalex.org/W3196904463","https://openalex.org/W4225010644","https://openalex.org/W4226320329","https://openalex.org/W4285211446","https://openalex.org/W4292347849","https://openalex.org/W4312820606","https://openalex.org/W4312933868","https://openalex.org/W4313270795","https://openalex.org/W4366669969","https://openalex.org/W4390226181","https://openalex.org/W4394625793","https://openalex.org/W4402448712","https://openalex.org/W6685930055","https://openalex.org/W6760000479","https://openalex.org/W6797784111","https://openalex.org/W6855623072"],"related_works":["https://openalex.org/W2781569684","https://openalex.org/W2478098815","https://openalex.org/W4290692565","https://openalex.org/W2371486462","https://openalex.org/W1540410989","https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2012220638","https://openalex.org/W3186203716","https://openalex.org/W1522196789"],"abstract_inverted_index":{"There":[0],"are":[1,52],"a":[2,68,134],"huge":[3],"number":[4],"of":[5,88,130,183],"models":[6,19],"that":[7,76,146],"claim":[8],"to":[9,62,80,136,143],"enhance":[10],"visual":[11],"surface":[12,57,195],"defect":[13,58,82,131,196],"inspection":[14],"accuracy.":[15],"However,":[16],"as":[17],"these":[18,63],"generally":[20],"function":[21],"directly":[22],"within":[23],"the":[24,85,89,108,124,128,166,172,181],"pixel":[25],"space,":[26],"optimizing":[27],"advanced":[28],"segmentation":[29,100],"techniques":[30],"frequently":[31],"demands":[32],"substantial":[33],"computational":[34],"resources":[35],"and":[36,99,114,140,159,168,186],"poses":[37],"challenges":[38,125],"for":[39,110,194],"inference":[40],"on":[41,55,72,165],"devices":[42],"with":[43],"limited":[44],"computing":[45],"power.":[46],"In":[47,60],"addition,":[48],"many":[49],"current":[50],"methodologies":[51],"deeply":[53],"reliant":[54],"extensive":[56],"datasets.":[59],"response":[61,112],"challenges,":[64],"our":[65,138],"research":[66],"presents":[67],"novel":[69],"approach":[70,139],"based":[71],"an":[73],"auto-encoder":[74],"structure":[75],"uses":[77],"\u201clatent":[78],"space\u201d":[79],"refine":[81],"segmentation.":[83,197],"Within":[84],"encoder":[86],"segment":[87],"autoencoder,":[90],"we\u2019ve":[91],"incorporated":[92],"contrastive":[93,187],"learning,":[94,188],"amplifying":[95],"both":[96],"feature":[97],"extraction":[98],"capabilities.":[101],"This":[102],"architectural":[103],"choice":[104],"not":[105],"only":[106],"tailors":[107],"strategy":[109],"prompt":[111],"scenarios":[113],"underscores":[115],"its":[116],"precision":[117],"in":[118],"high-accuracy":[119],"applications,":[120],"but":[121],"also":[122],"addresses":[123],"posed":[126],"by":[127],"scarcity":[129],"samples.":[132],"As":[133],"means":[135],"assess":[137],"better":[141],"cater":[142],"industrial":[144],"applications":[145],"prioritize":[147],"sample-level":[148,153],"accuracy,":[149],"we":[150],"introduce":[151],"innovative":[152],"metrics,":[154],"namely,":[155],"mostly":[156,160],"segmented":[157],"(MS)":[158],"lost":[161],"(ML).":[162],"Experiments":[163],"conducted":[164],"RSDD":[167],"Neuseg":[169],"datasets":[170],"underscore":[171],"strategy\u2019s":[173],"steadfast":[174],"performance":[175],"under":[176],"diverse":[177],"data":[178],"circumstances.":[179],"Synthesizing":[180],"benefits":[182],"latent":[184],"space":[185],"this":[189],"article":[190],"delineates":[191],"proficient":[192],"methodology":[193]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}