{"id":"https://openalex.org/W4283722801","doi":"https://doi.org/10.1109/i2mtc48687.2022.9806552","title":"Modified YOLOv4 Framework with Thermal Images for Pedestrian Detection","display_name":"Modified YOLOv4 Framework with Thermal Images for Pedestrian Detection","publication_year":2022,"publication_date":"2022-05-16","ids":{"openalex":"https://openalex.org/W4283722801","doi":"https://doi.org/10.1109/i2mtc48687.2022.9806552"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc48687.2022.9806552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc48687.2022.9806552","pdf_url":null,"source":{"id":"https://openalex.org/S4363607934","display_name":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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/A5045150662","display_name":"Saurav Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Saurav Kumar","raw_affiliation_strings":["Indian Institute of Technology Roorkee,Department of Electrical Engineering,Roorkee,India","Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee,Department of Electrical Engineering,Roorkee,India","institution_ids":["https://openalex.org/I154851008"]},{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075622546","display_name":"P. Sumathi","orcid":"https://orcid.org/0000-0002-9556-6431"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"P. Sumathi","raw_affiliation_strings":["Indian Institute of Technology Roorkee,Department of Electrical Engineering,Roorkee,India","Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee,Department of Electrical Engineering,Roorkee,India","institution_ids":["https://openalex.org/I154851008"]},{"raw_affiliation_string":"Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5045150662"],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":null,"apc_paid":null,"fwci":0.2998,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.62334236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9998000264167786,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9986000061035156,"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/intersection","display_name":"Intersection (aeronautics)","score":0.7315371632575989},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6927406191825867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6574424505233765},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.558795154094696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5248315930366516},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4442002773284912},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4398641288280487},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4226100444793701},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3302207887172699},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19455933570861816},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1230473518371582},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.08959001302719116}],"concepts":[{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.7315371632575989},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6927406191825867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6574424505233765},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.558795154094696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5248315930366516},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4442002773284912},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4398641288280487},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4226100444793701},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3302207887172699},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19455933570861816},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1230473518371582},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08959001302719116},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/i2mtc48687.2022.9806552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc48687.2022.9806552","pdf_url":null,"source":{"id":"https://openalex.org/S4363607934","display_name":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W799490484","https://openalex.org/W2031489346","https://openalex.org/W2120339771","https://openalex.org/W2150066425","https://openalex.org/W2243422958","https://openalex.org/W2337756001","https://openalex.org/W2415234561","https://openalex.org/W2497039038","https://openalex.org/W2604319603","https://openalex.org/W2789621390","https://openalex.org/W2791724282","https://openalex.org/W2884367402","https://openalex.org/W2900482393","https://openalex.org/W2992308087","https://openalex.org/W3013274043","https://openalex.org/W3018757597","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W6622794119","https://openalex.org/W6723816956","https://openalex.org/W6750227808","https://openalex.org/W6777046832","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581"],"abstract_inverted_index":{"A":[0],"modified":[1,89],"YOLOv4":[2,90],"framework":[3,58,91],"is":[4,10],"proposed":[5],"for":[6,92],"pedestrian":[7,93],"detection":[8,94],"which":[9],"trained":[11],"and":[12,26,48],"tested":[13],"with":[14,59],"a":[15,64],"portion":[16],"of":[17,24,39,46,51,66,69,73,77,79,87],"ZUT-FIR-ADAS":[18],"thermal":[19,60],"image":[20],"dataset.":[21],"The":[22,42,81],"effectiveness":[23,86],"mosaic":[25],"cutmix":[27],"data":[28],"augmentation":[29],"techniques":[30],"have":[31,53],"been":[32,54],"utilized":[33],"to":[34],"improve":[35],"the":[36,85,88],"generalization":[37],"capability":[38],"this":[40,57],"framework.":[41],"mean":[43],"average":[44,49,75],"precision":[45,65],"73%":[47],"loss":[50],"1.8062":[52],"achieved":[55],"through":[56],"images.":[61],"It":[62],"yields":[63],"0.92,":[67],"recall":[68],"0.96,":[70],"F1":[71],"score":[72],"0.94,":[74],"intersection":[76],"union":[78],"72.25%.":[80],"experimental":[82],"results":[83],"show":[84],"under":[95],"diverse":[96],"weather":[97],"conditions.":[98]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}