{"id":"https://openalex.org/W4312803096","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892765","title":"YOLO-Xweld: Efficiently Detecting Pipeline Welding Defects in X-Ray Images for Constrained Environments","display_name":"YOLO-Xweld: Efficiently Detecting Pipeline Welding Defects in X-Ray Images for Constrained Environments","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312803096","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892765"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892765","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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/A5100381075","display_name":"Jun Yang","orcid":"https://orcid.org/0000-0003-2807-6130"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Yang","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China","School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008177874","display_name":"Bo Fu","orcid":"https://orcid.org/0000-0001-5900-4354"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Fu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070016264","display_name":"Jinquan Zeng","orcid":"https://orcid.org/0009-0000-6505-1755"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinquan Zeng","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China","School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100566969","display_name":"Shengxi Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shengxi Wu","raw_affiliation_strings":["College of Engineering, Carnegie Mellon University,Pittsburgh,United States","College of Engineering, Carnegie Mellon University, Pittsburgh, United States"],"affiliations":[{"raw_affiliation_string":"College of Engineering, Carnegie Mellon University,Pittsburgh,United States","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"College of Engineering, Carnegie Mellon University, Pittsburgh, United States","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100381075"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":8.6451,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.98842816,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10834","display_name":"Welding Techniques and Residual Stresses","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.996999979019165,"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"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/pipeline","display_name":"Pipeline (software)","score":0.7835817337036133},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6238529682159424},{"id":"https://openalex.org/keywords/welding","display_name":"Welding","score":0.5642030835151672},{"id":"https://openalex.org/keywords/operability","display_name":"Operability","score":0.49570024013519287},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.48572736978530884},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.47516363859176636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4680691957473755},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4314725995063782},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3482872247695923},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.25637248158454895},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21853971481323242},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1829444169998169},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.09435099363327026}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7835817337036133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6238529682159424},{"id":"https://openalex.org/C19474535","wikidata":"https://www.wikidata.org/wiki/Q131172","display_name":"Welding","level":2,"score":0.5642030835151672},{"id":"https://openalex.org/C126231374","wikidata":"https://www.wikidata.org/wiki/Q1061298","display_name":"Operability","level":2,"score":0.49570024013519287},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.48572736978530884},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.47516363859176636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4680691957473755},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4314725995063782},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3482872247695923},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25637248158454895},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21853971481323242},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1829444169998169},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.09435099363327026},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892765","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1923115158","https://openalex.org/W1976644809","https://openalex.org/W1985037657","https://openalex.org/W1985177823","https://openalex.org/W2074216025","https://openalex.org/W2074728300","https://openalex.org/W2102605133","https://openalex.org/W2132763323","https://openalex.org/W2341528187","https://openalex.org/W2509065397","https://openalex.org/W2570343428","https://openalex.org/W2607037079","https://openalex.org/W2618106639","https://openalex.org/W2911923560","https://openalex.org/W2912272978","https://openalex.org/W2919115771","https://openalex.org/W2962632463","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W3021151617","https://openalex.org/W3038713101","https://openalex.org/W3039856203","https://openalex.org/W3040156639","https://openalex.org/W3088105316","https://openalex.org/W3098357269","https://openalex.org/W3101998545","https://openalex.org/W3120048558","https://openalex.org/W3175083350","https://openalex.org/W4236137412"],"related_works":["https://openalex.org/W2045316876","https://openalex.org/W2736987351","https://openalex.org/W2025974577","https://openalex.org/W2255799226","https://openalex.org/W1486626671","https://openalex.org/W2661918375","https://openalex.org/W1783723958","https://openalex.org/W1991104667","https://openalex.org/W2068071846","https://openalex.org/W3088721469"],"abstract_inverted_index":{"X-ray":[0,22,102],"inspection":[1],"of":[2,13,99,127,148,162],"pipeline":[3,63,100,183],"welds":[4],"brings":[5],"benefits":[6],"for":[7,38,66],"the":[8,82,116,119,125,128,140,149,153,167,174,181],"industrial":[9],"quality":[10],"and":[11,17,31,40,90,114,131,139,172,176],"safety":[12],"production.":[14],"However,":[15],"noise":[16],"complex":[18],"texture":[19],"structure":[20],"in":[21,50,165,180],"images":[23],"caused":[24],"by":[25,76,136],"issues":[26],"such":[27],"as":[28],"poor":[29],"contrast":[30],"blurry":[32],"defects":[33,65],"lead":[34],"to":[35,43,61,81,85,109,146,158],"extraordinary":[36],"obstacles":[37],"lightweight":[39,112],"high-precision":[41],"identification":[42],"satisfy":[44],"almost":[45],"100%":[46],"accuracy":[47,155],"requirements,":[48],"especially":[49],"constrained":[51,67,182],"environments.":[52],"This":[53,160],"paper":[54],"proposes":[55],"a":[56,111],"novel":[57],"method,":[58],"named":[59],"YOLO-Xweld,":[60],"detect":[62],"welding":[64],"application":[68],"scenarios.":[69],"YOLO-Xweld":[70],"is":[71,107,156,161],"derived":[72],"from":[73],"YOLO":[74],"v3-tiny":[75],"adding":[77],"an":[78],"SPP":[79],"module":[80],"backbone":[83],"network":[84,93],"enhance":[86],"multi-scale":[87],"feature":[88],"extraction,":[89],"weakening":[91],"large-scale":[92],"detection":[94,185],"branch":[95],"based":[96],"on":[97,118,169],"characteristics":[98],"weld":[101,184],"images.":[103],"Depthwise":[104],"separable":[105],"convolution":[106],"introduced":[108],"achieve":[110],"model":[113,141],"reduce":[115],"dependence":[117,168],"high-performance":[120,170],"hardware.":[121],"Experiments":[122],"show":[123],"that":[124],"number":[126],"model's":[129],"parameters":[130],"floating-point":[132],"operations":[133],"have":[134],"dropped":[135],"over":[137],"70%,":[138],"size":[142],"has":[143],"been":[144],"reduced":[145],"1/15":[147],"original":[150],"size,":[151],"while":[152],"test":[154],"close":[157],"100%.":[159],"great":[163],"significance":[164],"reducing":[166],"hardware":[171],"enhancing":[173],"convenience":[175],"operability":[177],"during":[178],"inspections":[179],"environment.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}