{"id":"https://openalex.org/W4415034603","doi":"https://doi.org/10.1109/netact65906.2025.11187900","title":"Vision Based Real Time Collision Detection Using MobileNetV2 and AI","display_name":"Vision Based Real Time Collision Detection Using MobileNetV2 and AI","publication_year":2025,"publication_date":"2025-08-07","ids":{"openalex":"https://openalex.org/W4415034603","doi":"https://doi.org/10.1109/netact65906.2025.11187900"},"language":"en","primary_location":{"id":"doi:10.1109/netact65906.2025.11187900","doi":"https://doi.org/10.1109/netact65906.2025.11187900","is_oa":false,"landing_page_url":"https://doi.org/10.1109/netact65906.2025.11187900","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Networks & Advances in Computational Technologies (NetACT)","raw_type":"proceedings-article"},"volume":null,"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":null,"display_name":"Devi Prasad Karthik P","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4387154592","display_name":"St. Joseph\u2019s College of Engineering","ror":"https://ror.org/01g3pby21","country_code":"IN","type":"education","lineage":["https://openalex.org/I4387154592"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Devi Prasad Karthik P","raw_affiliation_strings":["St. Joseph's College of Engineering,Department of Computer Science and Engineering,Chennai,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"St. Joseph's College of Engineering,Department of Computer Science and Engineering,Chennai,India","institution_ids":["https://openalex.org/I4387154592"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5152867025","display_name":"Tarun Teja M","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I145286018","display_name":"SRM Institute of Science and Technology","ror":"https://ror.org/050113w36","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Tarun Teja M","raw_affiliation_strings":["SRM Institute of Science and Technology,Department of Data Science and Business Systems,Chennai,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SRM Institute of Science and Technology,Department of Data Science and Business Systems,Chennai,India","institution_ids":["https://openalex.org/I145286018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40621191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.79830002784729,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.79830002784729,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.18359999358654022,"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/T10370","display_name":"Traffic and Road Safety","score":0.005900000222027302,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/traffic-accident-detection","display_name":"traffic accident detection","score":0.7799999713897705},{"id":"https://openalex.org/keywords/real-time-collision-detection","display_name":"real-time collision detection","score":0.8479999899864197},{"id":"https://openalex.org/keywords/mobilenetv2","display_name":"MobileNetV2","score":0.9639999866485596},{"id":"https://openalex.org/keywords/video-stream-processing","display_name":"video stream processing","score":0.5130000114440918},{"id":"https://openalex.org/keywords/road-safety","display_name":"road safety","score":0.7070000171661377}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6665999889373779},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5834000110626221},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5281999707221985},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46239998936653137},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.43959999084472656},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.4163999855518341},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4041999876499176},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C2780289543","wikidata":"https://www.wikidata.org/wiki/Q424630","display_name":"Accident (philosophy)","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.33309999108314514},{"id":"https://openalex.org/C3018537244","wikidata":"https://www.wikidata.org/wiki/Q242256","display_name":"Video monitoring","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.3246999979019165},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C3017997152","wikidata":"https://www.wikidata.org/wiki/Q814610","display_name":"Emergency response","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C28472234","wikidata":"https://www.wikidata.org/wiki/Q213666","display_name":"Real-time operating system","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/netact65906.2025.11187900","doi":"https://doi.org/10.1109/netact65906.2025.11187900","is_oa":false,"landing_page_url":"https://doi.org/10.1109/netact65906.2025.11187900","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Networks & Advances in Computational Technologies (NetACT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5142999887466431,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2963163009","https://openalex.org/W4293363567"],"related_works":[],"abstract_inverted_index":{"Globally,":[0],"one":[1],"of":[2,6,21,83,98],"the":[3,69,81,101,109],"main":[4],"causes":[5],"death":[7],"is":[8,18,103,116],"road":[9],"accidents.":[10],"In":[11],"order":[12],"to":[13,68],"improve":[14],"emergency":[15],"response,":[16],"there":[17],"a":[19,33,96],"need":[20],"an":[22],"effective":[23],"and":[24,44,91],"real-time":[25,63],"accident":[26,99],"detection":[27],"system.":[28],"To":[29,106],"detect":[30],"realtime":[31],"accident,":[32],"deep":[34,59],"learning-based":[35],"approach":[36,111],"proposed":[37],"in":[38,48],"this":[39,56,84,114],"paper.":[40],"It":[41],"uses":[42],"MobileNetV2":[43],"camera":[45],"modules":[46],"placed":[47],"accident-prone":[49],"locations.":[50],"Live":[51],"video":[52],"streams":[53],"processed":[54],"by":[55,66],"system":[57,102],"through":[58],"learning":[60,122],"models.":[61],"Then,":[62],"alerts":[64],"sent":[65],"it":[67],"control":[70],"rooms.":[71],"Response":[72],"time":[73],"has":[74],"been":[75],"reduced":[76],"also":[77,104,117],"save":[78],"lives":[79],"with":[80,119],"help":[82,97],"approach.":[85],"Via":[86],"heavy":[87],"training,":[88],"high":[89],"strength":[90],"accuracy":[92],"are":[93],"realized.":[94],"With":[95],"datasets,":[100],"verified.":[105],"ensure":[107],"that":[108],"MobileNetV2-based":[110],"works":[112],"well,":[113],"work":[115],"compared":[118],"other":[120],"machine":[121],"approaches.":[123]},"counts_by_year":[],"updated_date":"2026-10-08T09:06:37.294493","created_date":"2025-10-10T00:00:00"}