{"id":"https://openalex.org/W7131176841","doi":"https://doi.org/10.1109/iccvw69036.2025.00089","title":"Fourier Domain Adaptation for Traffic Light Detection in Adverse Weather*","display_name":"Fourier Domain Adaptation for Traffic Light Detection in Adverse Weather*","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131176841","doi":"https://doi.org/10.1109/iccvw69036.2025.00089"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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/A5094237128","display_name":"Ishaan Gakhar","orcid":"https://orcid.org/0009-0001-7781-8287"},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ishaan Gakhar","raw_affiliation_strings":["Manipal Institute of Technology, Manipal Academy of Higher Education,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology, Manipal Academy of Higher Education,India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Aryaman Gupta","orcid":null},"institutions":[{"id":"https://openalex.org/I164861460","display_name":"Manipal Academy of Higher Education","ror":"https://ror.org/02xzytt36","country_code":"IN","type":"education","lineage":["https://openalex.org/I164861460"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aryaman Gupta","raw_affiliation_strings":["Manipal Institute of Technology, Manipal Academy of Higher Education,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology, Manipal Academy of Higher Education,India","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Aryesh Guha","orcid":null},"institutions":[{"id":"https://openalex.org/I57615478","display_name":"Mahle (Austria)","ror":"https://ror.org/0039sga55","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210138662","https://openalex.org/I57615478"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Aryesh Guha","raw_affiliation_strings":["MIT,Dept. of Electrical and Electronics Engineering,MAHE,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT,Dept. of Electrical and Electronics Engineering,MAHE,India","institution_ids":["https://openalex.org/I57615478"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126640542","display_name":"Amit Agarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I166794780","display_name":"Wells Fargo (United States)","ror":"https://ror.org/037r2ff59","country_code":"US","type":"company","lineage":["https://openalex.org/I166794780"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amit Agarwal","raw_affiliation_strings":["Wells Fargo ISPL,Enterprise Analytics &#x0026; Data Science AI, Center of Excellence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wells Fargo ISPL,Enterprise Analytics &#x0026; Data Science AI, Center of Excellence","institution_ids":["https://openalex.org/I166794780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079870633","display_name":"Ujjwal Verma","orcid":"https://orcid.org/0000-0002-6133-5379"},"institutions":[{"id":"https://openalex.org/I57615478","display_name":"Mahle (Austria)","ror":"https://ror.org/0039sga55","country_code":"AT","type":"company","lineage":["https://openalex.org/I4210138662","https://openalex.org/I57615478"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Ujjwal Verma","raw_affiliation_strings":["MIT,Dept. of Electronics and Communication Engineering,MAHE,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MIT,Dept. of Electronics and Communication Engineering,MAHE,India","institution_ids":["https://openalex.org/I57615478"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8699,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88877371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"816","last_page":"825"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.7886999845504761,"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/T11019","display_name":"Image Enhancement Techniques","score":0.7886999845504761,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.016499999910593033,"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/T11996","display_name":"Random lasers and scattering media","score":0.013399999588727951,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7864000201225281},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6292999982833862},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.48570001125335693},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.45190000534057617},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4269999861717224},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.42570000886917114},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.40130001306533813},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.3725999891757965},{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.36890000104904175}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7864000201225281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7760000228881836},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6292999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5038999915122986},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.45190000534057617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4494999945163727},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42590001225471497},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C2993660032","wikidata":"https://www.wikidata.org/wiki/Q746984","display_name":"Traffic speed","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.2597000002861023}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.7445756196975708}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1562810981","https://openalex.org/W2096070062","https://openalex.org/W2593768305","https://openalex.org/W2889945482","https://openalex.org/W2903632709","https://openalex.org/W2913979809","https://openalex.org/W2921469264","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W3000171824","https://openalex.org/W3022917557","https://openalex.org/W3034543232","https://openalex.org/W3035294798","https://openalex.org/W3096609285","https://openalex.org/W3097953352","https://openalex.org/W3107432133","https://openalex.org/W3201193904","https://openalex.org/W4213267140","https://openalex.org/W4306160534","https://openalex.org/W4318049991","https://openalex.org/W4387638628","https://openalex.org/W4393186518","https://openalex.org/W4396495268","https://openalex.org/W4403118651","https://openalex.org/W4403331779"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"light":[1],"detection":[2],"under":[3,90,228],"adverse":[4,77,91,147,281],"weather":[5,78,148,282],"conditions":[6,173],"remains":[7],"largely":[8],"unexplored":[9],"in":[10,259,262,265,269,279,292],"ADAS":[11],"systems,":[12],"with":[13,123,171,175],"existing":[14],"approaches":[15],"relying":[16],"on":[17],"complex":[18],"deep":[19],"learning":[20],"methods":[21,194],"that":[22],"introduce":[23],"significant":[24],"computational":[25],"overheads":[26],"during":[27],"training":[28,41,121],"and":[29,52,62,82,126,183,197,222,243,267],"deployment.":[30],"This":[31,150],"paper":[32],"proposes":[33],"Fourier":[34],"Domain":[35],"Adaptation":[36],"(FDA),":[37],"which":[38,70],"requires":[39],"only":[40],"data":[42,125,170,213],"modifications":[43],"without":[44],"architectural":[45],"changes,":[46],"enabling":[47],"effective":[48],"adaptation":[49],"to":[50,72,116,142,168,189,200,211],"rainy":[51,196],"foggy":[53,198],"conditions.":[54,119,149,295],"FDA":[55],"minimizes":[56],"the":[57,74,109,129,137,157,202,217],"domain":[58,64,131,133,143,180],"gap":[59],"between":[60],"source":[61,130,179],"target":[63,203],"by":[65,146],"manipulation":[66],"of":[67,80,128,159,219,225,257],"frequency":[68,76],"components":[69,95,102],"allows":[71],"reduce":[73],"high":[75],"effects":[79,158],"fog":[81],"rain,":[83],"creating":[84],"a":[85,248],"dataset":[86],"for":[87,155],"reliable":[88,290],"performance":[89,224,291],"weather.":[92,230],"Since":[93],"low-frequency":[94],"encode":[96],"global":[97],"structural":[98,112],"information":[99],"while":[100,114],"high-frequency":[101],"capture":[103],"finer":[104],"details,":[105],"this":[106],"transformation":[107],"helps":[108],"model":[110,138],"retain":[111],"consistency":[113],"adapting":[115],"new":[117],"environmental":[118,294],"By":[120],"models":[122,227,235,238],"FDA-augmented":[124,234],"labels":[127,134],"(target":[132],"are":[135],"absent),":[136],"becomes":[139],"more":[140,214],"robust":[141],"shifts":[144],"caused":[145],"method":[151],"is":[152],"especially":[153],"helpful":[154],"mitigating":[156,280],"rain":[160],"or":[161],"fog,":[162],"as":[163],"it":[164],"can":[165],"be":[166],"challenging":[167,293],"find":[169],"these":[172],"along":[174],"proper":[176],"annotations.":[177],"The":[178],"merged":[181],"LISA":[182],"S<sup":[184],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[185],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>TLD":[186],"datasets,":[187],"processed":[188],"address":[190],"class":[191],"imbalance.":[192],"Established":[193],"simulated":[195],"scenarios":[199],"form":[201],"domain.":[204],"Semi-Supervised":[205],"Learning":[206],"(SSL)":[207],"techniques":[208],"were":[209,271],"explored":[210],"leverage":[212],"effectively,":[215],"addressing":[216],"shortage":[218],"comprehensive":[220],"datasets":[221],"poor":[223],"state-of-the-art":[226],"hostile":[229],"Experimental":[231],"results":[232],"show":[233],"outperform":[236],"baseline":[237],"across":[239,252,273],"mAP50,":[240,266],"mAP50-95,":[241],"Precision,":[242,260],"Recall":[244],"metrics.":[245,254],"YOLOv8":[246],"achieved":[247],"12.25%":[249],"average":[250],"increase":[251],"all":[253,274],"Average":[255],"improvements":[256,285],"7.69%":[258],"19.91%":[261],"Recall,":[263],"15.85%":[264],"23.81%":[268],"mAP50-95":[270],"observed":[272],"models,":[275],"demonstrating":[276],"FDA's":[277],"effectiveness":[278],"impact.":[283],"These":[284],"enable":[286],"real-world":[287],"applications":[288],"requiring":[289]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-24T00:00:00"}