{"id":"https://openalex.org/W2915017432","doi":"https://doi.org/10.1109/cisp-bmei.2018.8633119","title":"A Pedestrian Detection Method Based on YOLOv3 Model and Image Enhanced by Retinex","display_name":"A Pedestrian Detection Method Based on YOLOv3 Model and Image Enhanced by Retinex","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2915017432","doi":"https://doi.org/10.1109/cisp-bmei.2018.8633119","mag":"2915017432"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2018.8633119","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2018.8633119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5090156119","display_name":"Hongquan Qu","orcid":"https://orcid.org/0000-0002-2688-1470"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongquan Qu","raw_affiliation_strings":["North China University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China University of Technology, Beijing, China","institution_ids":["https://openalex.org/I1456306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054944178","display_name":"Tongyang Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongyang Yuan","raw_affiliation_strings":["North China University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China University of Technology, Beijing, China","institution_ids":["https://openalex.org/I1456306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035950527","display_name":"Zhiyong Sheng","orcid":null},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Sheng","raw_affiliation_strings":["North China University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China University of Technology, Beijing, China","institution_ids":["https://openalex.org/I1456306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100326592","display_name":"Yuan Zhang","orcid":"https://orcid.org/0000-0003-2726-2855"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Zhang","raw_affiliation_strings":["North China University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China University of Technology, Beijing, China","institution_ids":["https://openalex.org/I1456306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1456306"],"apc_list":null,"apc_paid":null,"fwci":1.369,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.90268494,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9990000128746033,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.8140980005264282},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7936022281646729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7854381799697876},{"id":"https://openalex.org/keywords/color-constancy","display_name":"Color constancy","score":0.7009133696556091},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.683756947517395},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6369888782501221},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6324610710144043},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6214240193367004},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.583078145980835},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4702020287513733},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.422288715839386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2962554693222046},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11224332451820374}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8140980005264282},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7936022281646729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7854381799697876},{"id":"https://openalex.org/C187888035","wikidata":"https://www.wikidata.org/wiki/Q2563885","display_name":"Color constancy","level":3,"score":0.7009133696556091},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.683756947517395},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6369888782501221},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6324610710144043},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6214240193367004},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.583078145980835},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4702020287513733},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.422288715839386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2962554693222046},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11224332451820374},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2018.8633119","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2018.8633119","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1999853363","https://openalex.org/W2020313904","https://openalex.org/W2147421915","https://openalex.org/W2162762921","https://openalex.org/W2570343428","https://openalex.org/W2796347433","https://openalex.org/W2953106684"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W2922437833","https://openalex.org/W4254103348"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,150,163,196],"is":[2,15,26,63],"a":[3,46,193],"basic":[4],"technology":[5,32],"in":[6,59],"the":[7,19,29,54,60,67,83,100,109,117,123,126,130,133,139,149,154,158,162,185,202,205],"field":[8],"of":[9,22,43,49,56,82,111,125,132,176,198],"intelligent":[10],"traffic":[11],"video":[12],"surveillance.":[13],"It":[14,25],"also":[16],"help":[17],"for":[18],"optimization":[20],"design":[21],"rail":[23],"transport.":[24],"known":[27],"that":[28,184],"deep":[30,84],"learning":[31],"can":[33,78],"achieve":[34],"considerable":[35],"performance":[36,81],"on":[37,99],"pedestrian":[38,75,174],"detection.":[39],"However,":[40],"this":[41,89,91],"kind":[42],"methods":[44],"demand":[45],"large":[47],"number":[48],"high-quality":[50],"samples.":[51],"In":[52],"addition,":[53],"quality":[55],"data":[57],"sample":[58,141,156,207],"subway":[61],"station":[62],"usually":[64],"sensitive":[65],"to":[66,103,107,121,147,160],"background":[68],"environment,":[69],"such":[70],"as":[71],"variant":[72],"illumination":[73],"or":[74],"density,":[76],"which":[77],"significantly":[79],"affect":[80],"neural":[85],"network.":[86],"To":[87],"solve":[88],"problem,":[90],"paper":[92],"adopts":[93],"an":[94],"image":[95,118,127,190],"enhancement":[96,119,191,206],"policy":[97],"based":[98],"Retinex":[101,189],"theory":[102],"preprocess":[104],"training":[105],"samples":[106],"reduce":[108],"influence":[110],"light":[112],"changes.":[113],"Firstly,":[114],"we":[115,137,167],"use":[116],"method":[120],"enhance":[122],"contrast":[124],"and":[128,152],"highlight":[129],"color":[131],"object":[134],"itself.":[135],"Next,":[136],"put":[138,153],"initial":[140],"into":[142,157],"darknet":[143],"frame":[144],"with":[145,172,201],"YOLOv3":[146,159],"train":[148,161],"model1":[151],"enhanced":[155],"model":[164,186,203],"2.":[165],"Finally,":[166],"tested":[168],"these":[169],"two":[170],"models":[171],"200":[173],"pictures":[175],"four":[177],"different":[178],"scenarios.":[179],"The":[180],"experimental":[181],"results":[182],"show":[183],"trained":[187],"by":[188],"has":[192],"more":[194],"accurate":[195],"rate":[197],"94%":[199],"compared":[200],"without":[204],"trained.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
