{"id":"https://openalex.org/W7128040304","doi":"https://doi.org/10.1007/s44163-026-00871-7","title":"Real-time traffic obstacle detection using hybrid FCL for R-CNN and EfficientDet with transfer learning","display_name":"Real-time traffic obstacle detection using hybrid FCL for R-CNN and EfficientDet with transfer learning","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7128040304","doi":"https://doi.org/10.1007/s44163-026-00871-7"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-026-00871-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-00871-7","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44163-026-00871-7","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125156576","display_name":"K. Veningston","orcid":null},"institutions":[{"id":"https://openalex.org/I8778637","display_name":"National Institute of Technology Srinagar","ror":"https://ror.org/03sfwvw54","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210152752","https://openalex.org/I8778637"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"K. Veningston","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India","institution_ids":["https://openalex.org/I8778637"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125234598","display_name":"M. Ronalda","orcid":null},"institutions":[{"id":"https://openalex.org/I8778637","display_name":"National Institute of Technology Srinagar","ror":"https://ror.org/03sfwvw54","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210152752","https://openalex.org/I8778637"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Ronalda","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India","institution_ids":["https://openalex.org/I8778637"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125128268","display_name":"R. Sathiyaraj","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":"R. Sathiyaraj","raw_affiliation_strings":["Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India, Karnataka"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India, Karnataka","institution_ids":["https://openalex.org/I164861460"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125232441","display_name":"C. Selvan","orcid":null},"institutions":[{"id":"https://openalex.org/I83737708","display_name":"REVA University","ror":"https://ror.org/03gtcxd54","country_code":"IN","type":"education","lineage":["https://openalex.org/I83737708"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"C. Selvan","raw_affiliation_strings":["Department of Computer Science and Engineering, Reva University, Bengaluru, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Reva University, Bengaluru, India","institution_ids":["https://openalex.org/I83737708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125108593","display_name":"P. V. Venkateswara Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I875944469","display_name":"Koneru Lakshmaiah Education Foundation","ror":"https://ror.org/02k949197","country_code":"IN","type":"education","lineage":["https://openalex.org/I875944469"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"P. V. Venkateswara Rao","raw_affiliation_strings":["Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India","institution_ids":["https://openalex.org/I875944469"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114337560","display_name":"Janardhan Karravula","orcid":null},"institutions":[{"id":"https://openalex.org/I47838141","display_name":"Saint Louis University","ror":"https://ror.org/01p7jjy08","country_code":"US","type":"education","lineage":["https://openalex.org/I47838141"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Janardhan Karravula","raw_affiliation_strings":["Saint Louis University, St. Louis, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Saint Louis University, St. Louis, USA","institution_ids":["https://openalex.org/I47838141"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5125156576"],"corresponding_institution_ids":["https://openalex.org/I8778637"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21028127,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7800999879837036,"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":0.7800999879837036,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.04560000076889992,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.026000000536441803,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/obstacle","display_name":"Obstacle","score":0.7278000116348267},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6912999749183655},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5935999751091003},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5020999908447266},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5008000135421753},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4740000069141388},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4277999997138977},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.41679999232292175},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.40220001339912415}],"concepts":[{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.7278000116348267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7217000126838684},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6912999749183655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.607200026512146},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5020999908447266},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5008000135421753},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.41449999809265137},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.310699999332428},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-026-00871-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-00871-7","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d62be15007d443f4a1dc3c5db17e2dfd","is_oa":true,"landing_page_url":"https://doaj.org/article/d62be15007d443f4a1dc3c5db17e2dfd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 6, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-026-00871-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-00871-7","pdf_url":null,"source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5635375380516052}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W2031489346","https://openalex.org/W2553754377","https://openalex.org/W2990268359","https://openalex.org/W3040318838","https://openalex.org/W3107610277","https://openalex.org/W4311033427","https://openalex.org/W4386076100","https://openalex.org/W4386116342","https://openalex.org/W4395030270","https://openalex.org/W4400418765","https://openalex.org/W4405270506","https://openalex.org/W4407793136","https://openalex.org/W4408619915","https://openalex.org/W4415196233"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"The":[1,58,95,116,150,253,264],"ability":[2],"of":[3,163,168,174,194,246],"vehicles":[4],"to":[5,241,250],"detect":[6],"obstacles":[7,221],"on":[8,77,144],"the":[9,70,78,136,145,182,204,233],"road":[10,49,220],"is":[11,27,61,72],"a":[12,28,62,73,160,171,190,243],"critical":[13],"component":[14],"in":[15,156,222],"advancing":[16],"autonomous":[17],"driving":[18],"systems.":[19,34],"Driving":[20],"involves":[21],"complex":[22],"perception":[23],"and":[24,32,40,55,69,114,170,228,235],"decision-making,":[25],"which":[26],"challenge":[29],"for":[30,46,92,125,200,238],"humans":[31],"automated":[33],"In":[35,81,134,180],"this":[36,82],"work,":[37,83],"we":[38],"present":[39,232],"evaluate":[41],"two":[42],"object":[43],"detection":[44,107,127,161,267],"models":[45,240],"identifying":[47],"various":[48],"entities,":[50],"including":[51],"cars,":[52],"trucks,":[53],"pedestrians,":[54],"other":[56],"obstacles.":[57],"first":[59],"model":[60,91,118,138,152,185,218,262],"modified":[63],"Region-based":[64],"Convolutional":[65],"Neural":[66],"Network":[67],"(R-CNN),":[68],"second":[71,178],"single-stage":[74],"detector":[75],"based":[76],"EfficientDet-D0":[79,137,151,265],"architecture.":[80],"R-CNN":[84,117,184],"uses":[85],"VGG-16":[86,103],"as":[87],"its":[88],"base":[89],"CNN":[90],"feature":[93],"extraction.":[94],"combination":[96],"provides":[97],"strong":[98],"representational":[99],"power":[100],"by":[101,270],"utilizing":[102],"with":[104,121,189],"structured":[105],"region-based":[106],"from":[108],"R-CNN,":[109],"enabling":[110],"accurate":[111],"obstacle":[112,208],"classification":[113],"localization.":[115],"was":[119,139],"enhanced":[120,261],"architectural":[122],"modifications":[123],"tailored":[124],"two-stage":[126],"using":[128,141],"hybrid":[129],"fully":[130],"connected":[131],"layers":[132],"(FCL).":[133],"contrast,":[135,181],"trained":[140],"transfer":[142,258,274],"learning":[143,259],"Udacity":[146],"self-driving":[147],"car":[148],"dataset.":[149],"demonstrated":[153,256],"superior":[154],"performance":[155,248],"real-time":[157,201],"conditions,":[158],"reporting":[159],"accuracy":[162,234,268],"76.8%":[164],"mAP@0.5,":[165,188],"an":[166],"IoU":[167],"0.73,":[169],"processing":[172,192],"speed":[173],"30":[175],"frames":[176],"per":[177],"(FPS).":[179],"custom":[183],"achieved":[186],"69.3%":[187],"notable":[191],"rate":[193],"32":[195],"FPS,":[196],"making":[197],"it":[198],"suitable":[199],"deployment.":[202],"Despite":[203],"promising":[205],"results,":[206],"certain":[207],"categories":[209],"remain":[210],"inadequately":[211],"detected":[212],"at":[213],"high":[214,229],"vehicle":[215],"speeds.":[216],"Our":[217],"detects":[219],"real-time,":[223],"achieving":[224],"both":[225,239],"low":[226],"latency":[227],"accuracy.":[230],"We":[231],"loss":[236],"metrics":[237],"provide":[242],"detailed":[244],"analysis":[245],"their":[247],"compared":[249],"baseline":[251],"methods.":[252],"ablation":[254],"study":[255],"that":[257],"significantly":[260],"performance.":[263],"model\u2019s":[266],"dropped":[269],"over":[271],"14%":[272],"without":[273],"learning.":[275]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-02-07T00:00:00"}
