{"id":"https://openalex.org/W4416249821","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228421","title":"LJ-DETR: LightWeight RT-DETR Algorithm with Joint Encoder for Steel Surface Defect Detection","display_name":"LJ-DETR: LightWeight RT-DETR Algorithm with Joint Encoder for Steel Surface Defect Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249821","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228421"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5001810307","display_name":"Chenyuan Wang","orcid":"https://orcid.org/0000-0002-2442-8584"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyuan Wang","raw_affiliation_strings":["Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115596179","display_name":"Tianqi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianqi Wang","raw_affiliation_strings":["Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079771801","display_name":"Huacheng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huacheng Li","raw_affiliation_strings":["Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100761956","display_name":"Mengyao Wang","orcid":"https://orcid.org/0009-0003-3810-8164"},"institutions":[{"id":"https://openalex.org/I46305995","display_name":"Taiyuan University of Science and Technology","ror":"https://ror.org/01wcbdc92","country_code":"CN","type":"education","lineage":["https://openalex.org/I46305995"]},{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengyao Wang","raw_affiliation_strings":["Taiyuan University of Technology,College of Computer Science and Technology,Jinzhong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Computer Science and Technology,Jinzhong,China","institution_ids":["https://openalex.org/I46305995","https://openalex.org/I9086337"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jingqi Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingqi Xia","raw_affiliation_strings":["Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China","institution_ids":["https://openalex.org/I9086337"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010202880","display_name":"Xilin Liu","orcid":"https://orcid.org/0000-0002-1136-6783"},"institutions":[{"id":"https://openalex.org/I9086337","display_name":"Taiyuan University of Technology","ror":"https://ror.org/03kv08d37","country_code":"CN","type":"education","lineage":["https://openalex.org/I9086337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xilin Liu","raw_affiliation_strings":["Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taiyuan University of Technology,College of Artificial Intelligence,Jinzhong,China","institution_ids":["https://openalex.org/I9086337"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.840499997138977,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.840499997138977,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.08579999953508377,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.014499999582767487,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/process","display_name":"Process (computing)","score":0.6564000248908997},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.6126000285148621},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5044000148773193},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4510999917984009},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4429999887943268},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.412200003862381}],"concepts":[{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6564000248908997},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6078000068664551},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4429999887943268},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3646000027656555},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C71405471","wikidata":"https://www.wikidata.org/wiki/Q757012","display_name":"Quality management","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3005000054836273},{"id":"https://openalex.org/C2991790204","wikidata":"https://www.wikidata.org/wiki/Q1727909","display_name":"Steel frame","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2621999979019165},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2531000077724457},{"id":"https://openalex.org/C2776542497","wikidata":"https://www.wikidata.org/wiki/Q5266672","display_name":"Development (topology)","level":2,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2752782242","https://openalex.org/W2911595607","https://openalex.org/W2963037989","https://openalex.org/W2988948211","https://openalex.org/W3034421924","https://openalex.org/W3195969653","https://openalex.org/W4206217610","https://openalex.org/W4225576932","https://openalex.org/W4289752563","https://openalex.org/W4294805043","https://openalex.org/W4313177792","https://openalex.org/W4327652243","https://openalex.org/W4402351079","https://openalex.org/W4402754006","https://openalex.org/W4403770406"],"related_works":[],"abstract_inverted_index":{"Steel":[0],"surface":[1,26],"defect":[2,193],"detection":[3,187],"is":[4,135],"a":[5,35,107,130,136],"crucial":[6],"aspect":[7],"of":[8,22,38,45,102,132],"the":[9,20,29,43,84,100,103,116,121,144,151,164,171,182],"steel":[10,23],"manufacturing":[11],"process,":[12],"serving":[13],"as":[14],"an":[15],"essential":[16],"guarantee":[17],"for":[18,185],"enhancing":[19,42],"quality":[21,44],"production.":[24],"Detecting":[25],"defects":[27],"during":[28],"industrial":[30,192],"production":[31],"process":[32],"has":[33],"become":[34],"critical":[36],"area":[37],"research":[39],"aimed":[40],"at":[41],"strip":[46],"steel.":[47],"Existing":[48],"methods":[49,63],"based":[50],"on":[51,99,115,163],"YOLO":[52,104],"demonstrate":[53,119],"strong":[54],"real-time":[55,73,186],"capabilities":[56],"but":[57],"often":[58],"lack":[59],"precision,":[60],"while":[61],"DETR":[62],"offer":[64],"higher":[65],"accuracy":[66,190],"yet":[67],"face":[68],"challenges":[69,91],"in":[70,191],"training,":[71],"low":[72],"performance,":[74],"and":[75,97,149,188],"substantial":[76],"computational":[77],"demands.":[78],"In":[79],"this":[80],"paper,":[81],"we":[82],"introduce":[83],"LJ-DETR":[85],"model,":[86],"designed":[87],"to":[88,147,156,170,179],"overcome":[89],"these":[90],"by":[92,167],"integrating":[93],"various":[94],"lightweight":[95],"modules":[96],"capitalizing":[98],"strengths":[101],"architecture":[105],"through":[106],"Joint":[108],"Hybrid":[109],"Encoder.":[110],"Our":[111,141],"extensive":[112],"experiments":[113],"conducted":[114],"NEU-DET":[117],"dataset":[118,166],"that":[120],"improved":[122],"model":[123,142,160],"significantly":[124],"surpasses":[125],"four":[126],"RT-DETR":[127],"baselines,":[128],"achieving":[129],"mAP@50":[131],"75.1%,":[133],"which":[134],"7.6%":[137],"improvement":[138],"over":[139],"RT-DETR-X.":[140],"reduces":[143],"parameter":[145],"count":[146],"20.5M":[148],"enhances":[150],"frames":[152],"per":[153],"second":[154],"(FPS)":[155],"166.7.":[157],"Additionally,":[158],"our":[159,177],"improves":[161],"performance":[162],"GC10-DET":[165],"4%":[168],"compared":[169],"best-performing":[172],"RT-DETR-R50.":[173],"These":[174],"advancements":[175],"enable":[176],"approach":[178],"effectively":[180],"meet":[181],"stringent":[183],"demands":[184],"high":[189],"inspection.":[194]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-14T00:00:00"}
