{"id":"https://openalex.org/W4387935028","doi":"https://doi.org/10.1109/lsp.2023.3327649","title":"GDiPAYOLO: A Fault Detection Algorithm for UAV Power Inspection Scenarios","display_name":"GDiPAYOLO: A Fault Detection Algorithm for UAV Power Inspection Scenarios","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4387935028","doi":"https://doi.org/10.1109/lsp.2023.3327649"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2023.3327649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2023.3327649","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5065651454","display_name":"Xiao Liang","orcid":"https://orcid.org/0000-0001-7391-4012"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao Liang","raw_affiliation_strings":["School of Information Engineering, Minzu University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Minzu University of China, Beijing, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100417470","display_name":"Jiali Wang","orcid":"https://orcid.org/0009-0008-7264-7424"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiali Wang","raw_affiliation_strings":["Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102994922","display_name":"Peidong Xu","orcid":"https://orcid.org/0009-0005-5734-3009"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peidong Xu","raw_affiliation_strings":["Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101488452","display_name":"Qingyu Kong","orcid":"https://orcid.org/0000-0002-9948-7036"},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyu Kong","raw_affiliation_strings":["State Grid Laboratory of Grid Advanced Computing and Applications (State Grid Smart Grid Research Institute Company Ltd.), Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Grid Laboratory of Grid Advanced Computing and Applications (State Grid Smart Grid Research Institute Company Ltd.), Beijing, China","institution_ids":["https://openalex.org/I17442442"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047041935","display_name":"Zhaogang Han","orcid":"https://orcid.org/0000-0003-3435-001X"},"institutions":[{"id":"https://openalex.org/I17442442","display_name":"State Grid Corporation of China (China)","ror":"https://ror.org/05twwhs70","country_code":"CN","type":"company","lineage":["https://openalex.org/I17442442"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaogang Han","raw_affiliation_strings":["Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (State Grid Smart Grid Research Institute Company Ltd.), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (State Grid Smart Grid Research Institute Company Ltd.), Beijing, China","institution_ids":["https://openalex.org/I17442442"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5065651454"],"corresponding_institution_ids":["https://openalex.org/I145897649"],"apc_list":null,"apc_paid":null,"fwci":1.4443,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.84438749,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"1577","last_page":"1581"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/computer-science","display_name":"Computer science","score":0.8047015070915222},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.594656765460968},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5912436842918396},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5386862754821777},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5381923317909241},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5300756692886353},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5157907009124756},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4999501705169678},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.47917428612709045},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4757808744907379},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46381184458732605},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4579443335533142},{"id":"https://openalex.org/keywords/performance-metric","display_name":"Performance metric","score":0.4513324201107025},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.412821888923645},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37753307819366455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3741404414176941},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2822723388671875},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15732920169830322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8047015070915222},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.594656765460968},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5912436842918396},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5386862754821777},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5381923317909241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5300756692886353},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5157907009124756},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4999501705169678},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.47917428612709045},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4757808744907379},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46381184458732605},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4579443335533142},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.4513324201107025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.412821888923645},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37753307819366455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3741404414176941},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2822723388671875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15732920169830322},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/lsp.2023.3327649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2023.3327649","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G241045384","display_name":null,"funder_award_id":"Z231100001723002","funder_id":"https://openalex.org/F4320335843","funder_display_name":"Beijing Science and Technology Planning Project"}],"funders":[{"id":"https://openalex.org/F4320335843","display_name":"Beijing Science and Technology Planning Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2002555321","https://openalex.org/W2024626428","https://openalex.org/W2618530766","https://openalex.org/W2963037989","https://openalex.org/W3005949141","https://openalex.org/W3014819769","https://openalex.org/W3092628553","https://openalex.org/W3098650625","https://openalex.org/W3105860898","https://openalex.org/W3111406415","https://openalex.org/W3120672808","https://openalex.org/W3122173535","https://openalex.org/W3200605425","https://openalex.org/W3201753566","https://openalex.org/W3213418101","https://openalex.org/W4205444143","https://openalex.org/W4211058054","https://openalex.org/W4281476198","https://openalex.org/W4296759963","https://openalex.org/W4297347768","https://openalex.org/W4323044392","https://openalex.org/W6773470269","https://openalex.org/W6775691121"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W147410782","https://openalex.org/W4287804464","https://openalex.org/W3022252430","https://openalex.org/W3103989898","https://openalex.org/W2152352598","https://openalex.org/W803346624","https://openalex.org/W3108539254"],"abstract_inverted_index":{"Numerous":[0],"real-time":[1,173],"object":[2,174],"detection":[3,175],"frameworks":[4,26],"have":[5],"been":[6],"evaluated":[7],"using":[8],"publicly":[9],"available":[10],"datasets,":[11],"demonstrating":[12],"commendable":[13],"performance.":[14],"However,":[15],"there":[16],"is":[17],"a":[18,118,158],"growing":[19],"need":[20],"for":[21,73,105],"improved":[22],"performance":[23,181],"when":[24,165],"these":[25],"are":[27],"applied":[28],"to":[29,35,127,150,162,167,171],"custom":[30],"datasets.":[31],"This":[32],"manuscript":[33],"aims":[34],"address":[36],"the":[37,49,74,85,123,129,145,154],"dual":[38],"objectives":[39],"of":[40,51,76,132,160,184],"achieving":[41],"high":[42],"accuracy":[43,187],"while":[44,152],"maintaining":[45],"lightweight":[46],"efficiency":[47],"in":[48,112,182],"context":[50],"UAV":[52,113],"(Unmanned":[53],"Aerial":[54],"Vehicle)":[55],"power":[56],"inspection":[57],"algorithm":[58],"development.":[59],"The":[60],"newly":[61],"introduced":[62],"GDiBlock":[63],"(Group":[64],"Dimensionality-increase":[65],"Convolution":[66],"Block)":[67],"effectively":[68],"reduces":[69,144],"channel":[70],"redundancy,":[71],"allowing":[72],"extraction":[75],"rich,":[77],"high-dimensional":[78],"feature":[79],"information":[80],"with":[81],"fewer":[82],"parameters.":[83],"Simultaneously,":[84],"PAM":[86],"(Pooling":[87],"Attention":[88],"Mechanism)":[89],"accurately":[90],"distinguishes":[91],"between":[92],"background":[93,108],"and":[94,188],"target":[95],"entities":[96],"without":[97],"introducing":[98],"additional":[99],"parameters,":[100],"making":[101],"it":[102],"particularly":[103],"advantageous":[104],"addressing":[106],"complex":[107],"scenarios":[109],"commonly":[110],"encountered":[111],"imagery.":[114],"We":[115],"meticulously":[116],"explore":[117],"design":[119],"paradigm":[120],"guided":[121],"by":[122,148,157],"receptive":[124],"field":[125],"principle":[126],"determine":[128],"optimal":[130],"number":[131],"blocks":[133],"within":[134],"each":[135],"processing":[136],"stage.":[137],"In":[138],"conclusion,":[139],"our":[140,177],"proposed":[141],"network":[142],"architecture":[143],"parameter":[146],"count":[147],"28%":[149],"40%,":[151],"improving":[153],"mAP@.5":[155],"metric":[156],"margin":[159],"1.7":[161],"5.4":[163],"points":[164],"compared":[166,170],"YOLOv5.":[168],"When":[169],"state-of-the-art":[172],"frameworks,":[176],"study":[178],"demonstrates":[179],"superior":[180],"terms":[183],"both":[185],"model":[186],"size":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}