{"id":"https://openalex.org/W4408092648","doi":"https://doi.org/10.1109/hipcw63042.2024.00018","title":"Object Detection for Autonomous Vehicles in Adverse Weather and Varying Lighting Conditions Using a Hybrid YOLO Approach","display_name":"Object Detection for Autonomous Vehicles in Adverse Weather and Varying Lighting Conditions Using a Hybrid YOLO Approach","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4408092648","doi":"https://doi.org/10.1109/hipcw63042.2024.00018"},"language":"en","primary_location":{"id":"doi:10.1109/hipcw63042.2024.00018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipcw63042.2024.00018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","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/A5089994345","display_name":"A N Saritha","orcid":"https://orcid.org/0009-0009-2318-3894"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saritha A N","raw_affiliation_strings":["NITK NITK,SPARK Lab,Department of CS&E,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NITK NITK,SPARK Lab,Department of CS&E,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004440264","display_name":"Basavaraj Talawar","orcid":"https://orcid.org/0000-0001-5054-3124"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Basavaraj Talawar","raw_affiliation_strings":["NITK NITK,SPARK Lab,Department of CS&E,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NITK NITK,SPARK Lab,Department of CS&E,India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2187,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55969259,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"52","last_page":"57"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9531999826431274,"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":0.9531999826431274,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9067999720573425,"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/adverse-weather","display_name":"Adverse weather","score":0.8235216736793518},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7179766893386841},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6863532066345215},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5875881910324097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5775067806243896},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5471566915512085},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3443217873573303},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.1956096887588501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.16268989443778992},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11079356074333191}],"concepts":[{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.8235216736793518},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179766893386841},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6863532066345215},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5875881910324097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5775067806243896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5471566915512085},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3443217873573303},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.1956096887588501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.16268989443778992},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11079356074333191}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hipcw63042.2024.00018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipcw63042.2024.00018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2730104357","https://openalex.org/W2353693354","https://openalex.org/W1933776184","https://openalex.org/W2166644210","https://openalex.org/W2484361849","https://openalex.org/W2761817071","https://openalex.org/W3153718224","https://openalex.org/W2737719445","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Object":[0],"Detection":[1],"is":[2,43,61,143],"a":[3,34,127,132],"major":[4],"task":[5],"in":[6],"Computer":[7],"Vision":[8],"with":[9,126],"applications":[10],"ranging":[11],"from":[12],"Surveillance":[13],"to":[14,49,69,80],"Autonomous":[15],"Vehicles.":[16],"In":[17],"the":[18,40,55,120,139],"detection":[19],"process,":[20],"manually":[21],"annotating":[22],"images":[23],"would":[24],"be":[25],"more":[26],"labor-intensive":[27],"and":[28,67,77,99,111,131],"time-consuming":[29],"particularly":[30],"if":[31],"we":[32],"have":[33],"large":[35],"dataset.":[36],"To":[37],"overcome":[38],"this,":[39],"YOLOv9":[41,72],"model":[42,60,65,122],"employed":[44],"as":[45],"an":[46],"annotation":[47,105],"technique":[48],"automate":[50],"image":[51],"labeling":[52,56,109],"that":[53,119,138],"accelerates":[54],"process.":[57],"The":[58],"YOLOv8":[59,87,121],"then":[62],"used":[63],"for":[64,83,145],"training":[66],"inference":[68],"detect":[70],"objects.":[71],"could":[73],"take":[74],"9":[75],"minutes":[76],"23":[78],"seconds":[79],"generate":[81],"class-labels":[82],"around":[84],"4.8K":[85],"images.":[86],"efficiently":[88],"detected":[89],"objects":[90],"across":[91],"five":[92],"classes":[93],"-":[94],"Cow,":[95],"person,":[96],"car,":[97],"truck":[98],"dog.":[100],"This":[101,136],"illustrated":[102],"how":[103],"semi-automated":[104],"can":[106],"significantly":[107],"reduce":[108],"time":[110],"effort":[112],"on":[113],"custom":[114],"datasets.":[115],"It":[116],"was":[117],"observed":[118],"achieved":[123],"good":[124],"performance":[125],"mAP.50":[128],"of":[129,134],"84.5%":[130],"mAP.50-95":[133],"70.1%.":[135],"demonstrates":[137],"hybrid":[140],"YOLO":[141],"approach":[142],"well-suited":[144],"real-time":[146],"object":[147],"detection.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}