{"id":"https://openalex.org/W4407474792","doi":"https://doi.org/10.1109/auteee62881.2024.10869678","title":"Research on Image Object Fault Detection Based on Deep Learning","display_name":"Research on Image Object Fault Detection Based on Deep Learning","publication_year":2024,"publication_date":"2024-12-27","ids":{"openalex":"https://openalex.org/W4407474792","doi":"https://doi.org/10.1109/auteee62881.2024.10869678"},"language":"en","primary_location":{"id":"doi:10.1109/auteee62881.2024.10869678","doi":"https://doi.org/10.1109/auteee62881.2024.10869678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/auteee62881.2024.10869678","pdf_url":null,"source":{"id":"https://openalex.org/S4363608018","display_name":"IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","raw_type":"proceedings-article"},"volume":null,"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/A5011276968","display_name":"Leilei Shi","orcid":"https://orcid.org/0009-0006-1395-2512","observed_orcids":["https://orcid.org/0009-0006-1395-2512"]},"institutions":[{"id":"https://openalex.org/I4405280962","display_name":"Shandong Vocational College of Light Industry","ror":"https://ror.org/03ng7ky76","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096525","https://openalex.org/I4387155179","https://openalex.org/I4405280962"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Leilei Shi","raw_affiliation_strings":["Shandong Vocational College of Light Industry,Zibo,Shandong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Vocational College of Light Industry,Zibo,Shandong,China","institution_ids":["https://openalex.org/I4405280962"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5011276968"],"corresponding_institution_ids":["https://openalex.org/I4405280962"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.42185914,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"359","last_page":"365"},"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":0.7426999807357788,"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":0.7426999807357788,"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":0.22419999539852142,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.02800000086426735,"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/attention-mechanism","display_name":"attention mechanism","score":0.9649999737739563},{"id":"https://openalex.org/keywords/multiscale-feature-fusion","display_name":"multi-scale feature fusion","score":0.9049999713897705},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.6759999990463257},{"id":"https://openalex.org/keywords/fault-identification","display_name":"fault identification","score":0.3959999978542328},{"id":"https://openalex.org/keywords/real-time-detection","display_name":"real-time detection","score":0.7689999938011169}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561590671539307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6781885623931885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5843221545219421},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5814091563224792},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5772489309310913},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4927556812763214},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.47646641731262207},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.44675469398498535},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4246484637260437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33188396692276},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10536152124404907},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/auteee62881.2024.10869678","doi":"https://doi.org/10.1109/auteee62881.2024.10869678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/auteee62881.2024.10869678","pdf_url":null,"source":{"id":"https://openalex.org/S4363608018","display_name":"IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Climate action","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/13"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W4254103348","https://openalex.org/W3210378990","https://openalex.org/W3034745255","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"In":[0,20,95,187],"recent":[1],"years,":[2],"with":[3,38,124,183],"the":[4,45,88,110,113,122,131,143,151,157,174,178,181,188,192,195,203,219,229,238,251,255,274,285,292],"rapid":[5],"development":[6],"of":[7,32,90,177,194,202,228,245,259],"deep":[8,53,62,82],"learning,":[9],"many":[10,21],"computer":[11,22],"vision":[12,23],"tasks":[13],"have":[14],"achieved":[15,217,236],"breakthroughs":[16],"again":[17],"and":[18,65,76,161,172,209,277],"again.":[19],"tasks,":[24],"fault":[25,42,48,93,99,114,123,170],"detection":[26,43,49,100,115,119,280],"has":[27,77,290],"been":[28],"widely":[29],"concerned":[30],"because":[31],"its":[33],"great":[34],"application":[35],"value.":[36],"Compared":[37],"traditional":[39],"image":[40,46,56,91],"target":[41,47,92],"methods,":[44],"method":[50,101,129,197,205,231,261],"based":[51,102,116],"on":[52,81,103,117,206,218,237],"learning":[54,83],"digs":[55],"features":[57,71],"layer":[58,60],"by":[59],"through":[61],"neural":[63],"networks,":[64],"can":[66,262,271],"learn":[67],"more":[68,167],"advanced":[69,293],"abstract":[70],"without":[72],"manual":[73],"feature":[74,140,147],"design,":[75],"better":[78],"generalization.":[79],"Based":[80],"technology,":[84],"this":[85,96,260,288],"paper":[86,289],"studies":[87],"algorithm":[89,286],"detection.":[94],"paper,":[97],"a":[98],"attention":[104,132,155],"mechanism":[105,133],"is":[106,198,216,223],"proposed":[107,196],"to":[108,134,138,156,248,268],"solve":[109],"problem":[111],"that":[112,150,165,201,227,284],"object":[118],"cannot":[120],"distinguish":[121],"not":[125],"obvious":[126,185],"features.":[127,186],"This":[128],"uses":[130],"assign":[135],"different":[136],"weights":[137],"each":[139],"graph":[141],"when":[142],"network":[144,152,179],"performs":[145],"multi-scale":[146],"fusion,":[148],"so":[149],"selectively":[153],"pays":[154],"higher-order":[158],"semantic":[159],"information":[160,164],"lower-order":[162],"detail":[163],"are":[166],"helpful":[168],"for":[169,180,250],"identification,":[171],"improves":[173],"recognition":[175],"ability":[176],"faults":[182],"less":[184],"experimental":[189],"verification":[190],"stage,":[191],"performance":[193],"higher":[199,225],"than":[200,226],"benchmark":[204,230,252],"both":[207],"private":[208,220],"public":[210],"datasets.":[211],"Among":[212],"them,":[213],"93.28%":[214],"mAP@all":[215],"dataset,":[221,242],"which":[222,270,282],"1.16%":[224],"(92.04%).":[232],"78.55%":[233],"mAP@0.5":[234],"was":[235],"publicly":[239],"available":[240],"VOC":[241],"an":[243],"improvement":[244],"1.48%":[246],"compared":[247],"77.07%":[249],"method.":[253],"At":[254],"same":[256],"time,":[257],"FPS":[258],"be":[263],"maintained":[264],"at":[265],"74,mAP@0.5":[266],"up":[267],"99.55%,":[269],"fully":[272],"meet":[273],"real-time":[275],"requirements":[276],"achieve":[278],"high":[279],"accuracy,":[281],"proves":[283],"in":[287],"reached":[291],"level.":[294]},"counts_by_year":[],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2025-10-10T00:00:00"}