{"id":"https://openalex.org/W7119030731","doi":"https://doi.org/10.3390/info17010052","title":"TransferLearning-Driven Large-Scale CNN Benchmarking with Explainable AI for Image-Based Dust Detection on Solar Panels","display_name":"TransferLearning-Driven Large-Scale CNN Benchmarking with Explainable AI for Image-Based Dust Detection on Solar Panels","publication_year":2026,"publication_date":"2026-01-06","ids":{"openalex":"https://openalex.org/W7119030731","doi":"https://doi.org/10.3390/info17010052"},"language":"en","primary_location":{"id":"doi:10.3390/info17010052","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17010052","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/info17010052","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049129632","display_name":"Hafeez Anwar","orcid":"https://orcid.org/0000-0001-9529-3966"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]},{"id":"https://openalex.org/I201384688","display_name":"National University of Computer and Emerging Sciences","ror":"https://ror.org/003eyb898","country_code":"PK","type":"education","lineage":["https://openalex.org/I201384688"]}],"countries":["DE","PK"],"is_corresponding":true,"raw_author_name":"Hafeez Anwar","raw_affiliation_strings":["Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST), Jamrud Road 160 Industrial Estate Road, Phase 1 Hayatabad, Peshawar 25100, Pakistan","Interdisciplinary Center for Digital Humanities and Social Sciences, Friedrich-Alexander University, 91054 Erlangen, Germany"],"raw_orcid":"https://orcid.org/0000-0001-9529-3966","affiliations":[{"raw_affiliation_string":"Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST), Jamrud Road 160 Industrial Estate Road, Phase 1 Hayatabad, Peshawar 25100, Pakistan","institution_ids":["https://openalex.org/I201384688"]},{"raw_affiliation_string":"Interdisciplinary Center for Digital Humanities and Social Sciences, Friedrich-Alexander University, 91054 Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5049129632"],"corresponding_institution_ids":["https://openalex.org/I181369854","https://openalex.org/I201384688"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0242527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"1","first_page":"52","last_page":"52"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.7160000205039978,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.7160000205039978,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.15639999508857727,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.008999999612569809,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8007000088691711},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7971000075340271},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5913000106811523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4726000130176544},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4632999897003174},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.39750000834465027},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39399999380111694}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8007000088691711},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7971000075340271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6107000112533569},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5913000106811523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5060999989509583},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4726000130176544},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4632999897003174},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.39750000834465027},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C2777618391","wikidata":"https://www.wikidata.org/wiki/Q1483757","display_name":"Solar power","level":3,"score":0.3634999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36090001463890076},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3156999945640564},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C541104983","wikidata":"https://www.wikidata.org/wiki/Q40015","display_name":"Solar energy","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.25110000371932983},{"id":"https://openalex.org/C2776748203","wikidata":"https://www.wikidata.org/wiki/Q83180","display_name":"Roof","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/info17010052","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17010052","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c71b1eae3f16402b834b9ba6ce1f8a35","is_oa":true,"landing_page_url":"https://doaj.org/article/c71b1eae3f16402b834b9ba6ce1f8a35","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 17, Iss 1, p 52 (2026)","raw_type":"article"},{"id":"pmh:oai:open.fau.de:openfau/38849","is_oa":true,"landing_page_url":"https://open.fau.de/bitstreams/bff257ef-9f3d-484c-ad98-6d9d3edd0a5c/download","pdf_url":null,"source":{"id":"https://openalex.org/S7407055110","display_name":"OPUS FAU - Online publication system of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/info17010052","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17010052","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4023870825767517,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W166727719","https://openalex.org/W1972082993","https://openalex.org/W2080955644","https://openalex.org/W2124386111","https://openalex.org/W2147141800","https://openalex.org/W2891503716","https://openalex.org/W2909806874","https://openalex.org/W2967028114","https://openalex.org/W3042075957","https://openalex.org/W3108632821","https://openalex.org/W3128878811","https://openalex.org/W4293676828","https://openalex.org/W4312201522","https://openalex.org/W4379052083","https://openalex.org/W4385707808","https://openalex.org/W4387164669","https://openalex.org/W4387589826","https://openalex.org/W4389158886","https://openalex.org/W4389785957","https://openalex.org/W4391176305","https://openalex.org/W4391521421","https://openalex.org/W4396652662","https://openalex.org/W4396718469","https://openalex.org/W4400727448","https://openalex.org/W4401547336","https://openalex.org/W4401879948","https://openalex.org/W4403186262","https://openalex.org/W4403678059","https://openalex.org/W4404599510","https://openalex.org/W4404983950","https://openalex.org/W4405398245","https://openalex.org/W4406231970","https://openalex.org/W4406910687","https://openalex.org/W4407599062","https://openalex.org/W4407767431","https://openalex.org/W4408126584","https://openalex.org/W4408261329"],"related_works":[],"abstract_inverted_index":{"Solar":[0],"panel":[1,138],"power":[2,30],"plants":[3],"are":[4,133],"typically":[5],"established":[6],"in":[7,17,160],"regions":[8],"with":[9,99,215,235],"maximum":[10],"solar":[11,96,123,137],"irradiation,":[12],"yet":[13],"these":[14,130,141],"conditions":[15],"result":[16],"heavy":[18],"dust":[19,40,120,209,302],"accumulation":[20],"on":[21,91,122,205,271],"the":[22,85,126,157,180,186,198,228,266,293],"panels":[23,97],"causing":[24],"significant":[25],"performance":[26,90,191],"degradation":[27],"and":[28,103,147,165,189,218,225,250,252],"reduced":[29],"output.":[31],"The":[32,168],"paper":[33],"addresses":[34],"this":[35,106],"issue":[36],"via":[37,278],"an":[38,272],"image-based":[39,119,301],"detection":[41,121,210,303],"solution":[42],"powered":[43],"by":[44,244],"deep":[45],"learning,":[46],"particularly":[47],"convolutional":[48],"neural":[49],"networks":[50],"(CNNs).":[51],"Most":[52],"of":[53,80,129,162,170,232,269,287,295],"such":[54,71,171,247,255],"solutions":[55],"use":[56,169],"state-of-the-art":[57],"CNNs":[58],"either":[59],"as":[60,248,256],"backbones/features":[61],"extractors,":[62],"or":[63],"propose":[64],"custom":[65],"models":[66,82,112,128,159],"built":[67],"upon":[68],"them.":[69],"Given":[70],"a":[72,77,149,172,176,206,279,284],"reliance,":[73],"future":[74,300],"research":[75],"requires":[76],"comprehensive":[78],"benchmarking":[79],"CNN":[81,111,131,297],"to":[83,101,115,135,155,196],"identify":[84],"ones":[86],"that":[87,113,240],"achieve":[88],"superior":[89,238],"classifying":[92],"clean":[93],"vs.":[94],"dusty":[95],"both":[98],"respect":[100],"accuracy":[102,164,286],"efficiency.":[104],"To":[105,259],"end,":[107],"we":[108,144,264],"evaluate":[109],"100":[110],"belong":[114],"16":[116],"families":[117],"for":[118,299],"panels,":[124],"where":[125,179],"pre-trained":[127],"architectures":[132],"used":[134],"encode":[136],"images.":[139],"Upon":[140],"image":[142,200,267,275],"encodings,":[143],"then":[145],"train":[146],"test":[148],"linear":[150],"support":[151],"vector":[152],"machine":[153],"(SVM)":[154],"determine":[156],"best-performing":[158],"terms":[161],"classification":[163,230],"training":[166],"time.":[167],"simple":[173],"classifier":[174,187],"ensures":[175],"fair":[177],"comparison":[178],"encodings":[181,268],"do":[182],"not":[183],"benefit":[184],"from":[185],"itself":[188],"their":[190,261],"reflects":[192],"each":[193],"CNN\u2019s":[194],"ability":[195],"capture":[197],"underlying":[199],"features.":[201],"Experiments":[202],"were":[203],"conducted":[204],"publicly":[207],"available":[208],"dataset,":[211],"using":[212],"stratified":[213],"shuffle-split":[214],"70\u201330,":[216],"80\u201320,":[217],"90\u201310":[219],"splits,":[220],"repeated":[221],"10":[222],"times.":[223],"convnext_xxlarge":[224],"resnetv2_152":[226,236,270],"achieved":[227,283],"best":[229],"rates":[231],"above":[233],"90%,":[234],"offering":[237],"efficiency":[239],"is":[241],"also":[242],"supported":[243],"features":[245],"analysis":[246],"tSNE":[249],"UMAP,":[251],"explainableAI":[253],"(XAI)":[254],"LIME":[257],"visualization.":[258],"prove":[260],"generalization":[262],"capability,":[263],"tested":[265],"unseen":[273],"real-world":[274],"dataset":[276],"captured":[277],"drone":[280],"camera,":[281],"which":[282],"remarkable":[285],"96%.":[288],"Consequently,":[289],"our":[290],"findings":[291],"guide":[292],"selection":[294],"optimal":[296],"backbones":[298],"systems.":[304]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-01-08T00:00:00"}
