{"id":"https://openalex.org/W4378976355","doi":"https://doi.org/10.1080/15567036.2023.2218301","title":"Detection of solar panel defects based on separable convolution and convolutional block attention module","display_name":"Detection of solar panel defects based on separable convolution and convolutional block attention module","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4378976355","doi":"https://doi.org/10.1080/15567036.2023.2218301"},"language":"en","primary_location":{"id":"doi:10.1080/15567036.2023.2218301","doi":"https://doi.org/10.1080/15567036.2023.2218301","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15567036.2023.2218301","pdf_url":null,"source":{"id":"https://openalex.org/S4210188153","display_name":"Energy Sources Part A Recovery Utilization and Environmental Effects","issn_l":"1556-7036","issn":["1556-7036","1556-7230"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Sources, Part A: Recovery, Utilization, and Environmental Effects","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5029780082","display_name":"Xiyun Yang","orcid":"https://orcid.org/0000-0003-0192-1437","observed_orcids":["https://orcid.org/0000-0003-0192-1437","https://orcid.org/0000-0002-7019-0239"]},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiyun Yang","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357522","display_name":"Qiao Zhang","orcid":"https://orcid.org/0000-0002-3509-8103","observed_orcids":["https://orcid.org/0000-0002-3509-8103"]},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiao Zhang","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115694757","display_name":"Shuyan Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyan Wang","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101901495","display_name":"Ya Zhao","orcid":"https://orcid.org/0009-0006-0196-1008","observed_orcids":["https://orcid.org/0009-0006-0196-1008"]},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ya Zhao","raw_affiliation_strings":["School of Control and Computer Engineering, North China Electric Power University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100357522"],"corresponding_institution_ids":["https://openalex.org/I153473198"],"apc_list":null,"apc_paid":null,"fwci":1.1095,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.7442247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":"45","issue":"3","first_page":"7136","last_page":"7149"},"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":0.9980000257492065,"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":0.9980000257492065,"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":0.0008999999845400453,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0005000000237487257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-panel-defect-detection","display_name":"solar panel defect detection","score":0.9860000014305115},{"id":"https://openalex.org/keywords/separable-convolution","display_name":"separable convolution","score":0.8289999961853027},{"id":"https://openalex.org/keywords/convolutional-block-attention-module","display_name":"CBAM","score":0.902999997138977},{"id":"https://openalex.org/keywords/vgg19","display_name":"VGG19","score":0.8939999938011169},{"id":"https://openalex.org/keywords/lightweight-neural-networks","display_name":"lightweight neural networks","score":0.781000018119812},{"id":"https://openalex.org/keywords/daubechies-4-wavelet","display_name":"Daubechies 4 wavelet","score":0.9089999794960022},{"id":"https://openalex.org/keywords/image-preprocessing","display_name":"image preprocessing","score":0.46700000762939453},{"id":"https://openalex.org/keywords/photovoltaic-power-generation","display_name":"photovoltaic power generation","score":0.8299999833106995}],"concepts":[{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.6586426496505737},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5874892473220825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5738421082496643},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5522829294204712},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.5230979919433594},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.49890971183776855},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44781824946403503},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.431460440158844},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4239403009414673},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.41955575346946716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3980003595352173},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3553280234336853},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3202759027481079},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2641775608062744},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24978327751159668},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12104406952857971},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11419945955276489},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11246973276138306},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15567036.2023.2218301","doi":"https://doi.org/10.1080/15567036.2023.2218301","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15567036.2023.2218301","pdf_url":null,"source":{"id":"https://openalex.org/S4210188153","display_name":"Energy Sources Part A Recovery Utilization and Environmental Effects","issn_l":"1556-7036","issn":["1556-7036","1556-7230"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-1","norway-1","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Energy Sources, Part A: Recovery, Utilization, and Environmental Effects","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9853000044822693,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"},{"score":0.7207000255584717,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2071448691","https://openalex.org/W2163605009","https://openalex.org/W2531409750","https://openalex.org/W2884585870","https://openalex.org/W2997909005","https://openalex.org/W3008632925","https://openalex.org/W3203305603","https://openalex.org/W4200549610","https://openalex.org/W4280496914","https://openalex.org/W4285082541","https://openalex.org/W4289535494","https://openalex.org/W4297833139","https://openalex.org/W4313677407","https://openalex.org/W4317748827","https://openalex.org/W4323976907","https://openalex.org/W4362508878"],"related_works":["https://openalex.org/W4377972109","https://openalex.org/W4414010291","https://openalex.org/W4405141386","https://openalex.org/W4392209963","https://openalex.org/W4411699705","https://openalex.org/W7123348349","https://openalex.org/W4387909309","https://openalex.org/W4415004018","https://openalex.org/W4403609898","https://openalex.org/W3126724134"],"abstract_inverted_index":{"The":[0,156,177],"share":[1],"of":[2,20,62,88,109,129,137,140,149,166,172,198],"renewable":[3],"energy":[4],"in":[5,23,33,96,153,202,207],"the":[6,60,86,97,107,117,127,147,150,161,196,199],"electricity":[7],"market":[8],"is":[9,15,56,123],"increasing":[10],"year":[11],"by":[12],"year.":[13],"It":[14],"necessary":[16],"to":[17,58,84,105,125,145],"identify":[18],"damage":[19],"solar":[21,28,42,64,89,130,141,204],"panels":[22,29,142],"a":[24,40,169,192],"timely":[25],"manner,":[26],"as":[27],"are":[30,69,99],"important":[31],"components":[32],"photovoltaic":[34],"power":[35],"generation.":[36],"In":[37],"this":[38],"paper,":[39],"lightweight":[41,193],"panel":[43,65,90,131,205],"fault":[44],"diagnosis":[45],"system":[46],"based":[47],"on":[48],"image":[49],"pre-processing":[50],"and":[51,81,112,168,183],"an":[52,164],"improved":[53,200],"VGG-19":[54,98,201],"network":[55,110],"proposed":[57,151,178],"address":[59],"problem":[61],"blurred":[63],"field":[66],"images,":[67],"which":[68],"not":[70],"easy":[71],"for":[72],"defects":[73,206],"detection.":[74,133,155],"First,":[75],"we":[76],"use":[77],"Daubechies":[78],"4(DB4)":[79],"wavelet":[80],"morphology-based":[82],"enhancements":[83],"improve":[85,113,126],"quality":[87],"images.":[91],"Then,":[92],"conventional":[93,189],"convolutional":[94,103],"layers":[95,104],"replaced":[100],"with":[101,191],"separable":[102],"reduce":[106],"number":[108],"parameters":[111],"training":[114],"efficiency.":[115],"Finally,":[116],"Convolutional":[118],"Block":[119],"Attention":[120],"Module":[121],"(CBAM)":[122],"introduced":[124],"accuracy":[128,165,182],"defects\u2019":[132],"A":[134],"dataset":[135],"consisting":[136],"3344":[138],"images":[139],"was":[143],"used":[144],"evaluate":[146],"performance":[148,186],"method":[152,162],"defect":[154],"experimental":[157],"results":[158],"show":[159],"that":[160],"has":[163,180],"87.8%":[167],"detection":[170],"speed":[171],"0.047":[173],"s":[174],"per":[175],"image.":[176],"model":[179],"higher":[181],"more":[184],"stable":[185],"than":[187],"other":[188],"networks":[190],"structure,":[194],"demonstrating":[195],"reliability":[197],"detecting":[203],"practical":[208],"applications.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6}],"updated_date":"2026-10-14T16:39:01.820549","created_date":"2023-06-02T00:00:00"}
