{"id":"https://openalex.org/W7150887659","doi":"https://doi.org/10.1080/01969722.2026.2645117","title":"Hybrid Fossa Eel and Grouper Optimizer Enabled Deep Learning for Road Anomaly Object Detection with Exponential Logarithmic-KNet-Based Object Segmentation","display_name":"Hybrid Fossa Eel and Grouper Optimizer Enabled Deep Learning for Road Anomaly Object Detection with Exponential Logarithmic-KNet-Based Object Segmentation","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7150887659","doi":"https://doi.org/10.1080/01969722.2026.2645117"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2026.2645117","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2026.2645117","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-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/A5057646533","display_name":"M. Somasekar","orcid":null},"institutions":[{"id":"https://openalex.org/I43814544","display_name":"Sathyabama Institute of Science and Technology","ror":"https://ror.org/01defpn95","country_code":"IN","type":"education","lineage":["https://openalex.org/I43814544"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Somasekar M.","raw_affiliation_strings":["Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology"],"raw_orcid":"https://orcid.org/0009-0002-9435-4944","affiliations":[{"raw_affiliation_string":"Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology","institution_ids":["https://openalex.org/I43814544"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133043531","display_name":"Sharanya C.","orcid":null},"institutions":[{"id":"https://openalex.org/I43814544","display_name":"Sathyabama Institute of Science and Technology","ror":"https://ror.org/01defpn95","country_code":"IN","type":"education","lineage":["https://openalex.org/I43814544"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sharanya C.","raw_affiliation_strings":["Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology","institution_ids":["https://openalex.org/I43814544"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082939817","display_name":"Rajesh Kumar S","orcid":null},"institutions":[{"id":"https://openalex.org/I338496084","display_name":"Sri Sai University","ror":"https://ror.org/01mkm2y61","country_code":"IN","type":"education","lineage":["https://openalex.org/I338496084"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajesh Kumar S.","raw_affiliation_strings":["Assistant Professor, Department of CSE (AI and ML), Sri Sai Ram Engineering College"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Assistant Professor, Department of CSE (AI and ML), Sri Sai Ram Engineering College","institution_ids":["https://openalex.org/I338496084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133051299","display_name":"Kumaravel T.","orcid":null},"institutions":[{"id":"https://openalex.org/I157674215","display_name":"Presidency University","ror":"https://ror.org/04xgbph11","country_code":"IN","type":"education","lineage":["https://openalex.org/I157674215"]},{"id":"https://openalex.org/I4210132279","display_name":"Presidency University","ror":"https://ror.org/03vzjn737","country_code":"BD","type":"education","lineage":["https://openalex.org/I4210132279"]}],"countries":["BD","IN"],"is_corresponding":false,"raw_author_name":"Kumaravel T.","raw_affiliation_strings":["Assistant Professor, Department of Computer Science, School of Engineering, Presidency University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Assistant Professor, Department of Computer Science, School of Engineering, Presidency University","institution_ids":["https://openalex.org/I157674215","https://openalex.org/I4210132279"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5057646533"],"corresponding_institution_ids":["https://openalex.org/I43814544"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31583554,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"57","issue":"4","first_page":"730","last_page":"764"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4936999976634979,"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":0.4936999976634979,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.09359999746084213,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.09080000221729279,"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/segmentation","display_name":"Segmentation","score":0.6274999976158142},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6247000098228455},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6065999865531921},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5297999978065491},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4805000126361847},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41850000619888306}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7642999887466431},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6274999976158142},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6262000203132629},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6247000098228455},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6065999865531921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5841000080108643},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5297999978065491},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35359999537467957},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.27709999680519104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2026.2645117","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2026.2645117","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2049004027","https://openalex.org/W2775131354","https://openalex.org/W2803574738","https://openalex.org/W2896942768","https://openalex.org/W2913427224","https://openalex.org/W3011285717","https://openalex.org/W3037130125","https://openalex.org/W3114997473","https://openalex.org/W3135141542","https://openalex.org/W3172778143","https://openalex.org/W3197046899","https://openalex.org/W4213339752","https://openalex.org/W4285819294","https://openalex.org/W4289913121","https://openalex.org/W4307494192","https://openalex.org/W4381163479","https://openalex.org/W4385324363","https://openalex.org/W4386864404","https://openalex.org/W4387745472","https://openalex.org/W4387959434","https://openalex.org/W4391768484","https://openalex.org/W4392007359","https://openalex.org/W4399207463","https://openalex.org/W4399732597","https://openalex.org/W4400234305","https://openalex.org/W4400475913","https://openalex.org/W4401245422","https://openalex.org/W4401702711","https://openalex.org/W4402865088","https://openalex.org/W4405441988","https://openalex.org/W4405786324","https://openalex.org/W4406571887","https://openalex.org/W4406614863","https://openalex.org/W4406832701","https://openalex.org/W4408400982"],"related_works":[],"abstract_inverted_index":{"In":[0,147],"recent":[1],"decades,":[2],"autonomous":[3],"driving":[4,24],"has":[5],"gained":[6],"significant":[7],"attention":[8],"across":[9],"industry":[10],"and":[11,45,141,143,163,177,185],"academia.":[12],"With":[13],"rising":[14],"vehicle":[15],"numbers,":[16],"ensuring":[17],"safety":[18],"is":[19,69,78,93,133],"vital.":[20],"Road":[21],"anomalies":[22],"unexpected":[23],"circumstances":[25],"remain":[26],"difficult":[27],"to":[28,171],"detect,":[29],"especially":[30],"when":[31],"small":[32],"or":[33],"distant.":[34],"To":[35],"address":[36],"these":[37],"challenges,":[38],"this":[39],"paper":[40],"proposes":[41],"a":[42,72],"Fossa":[43,137],"Eel":[44,142],"Group":[46],"Optimization-based":[47],"Composite":[48],"Backbone":[49],"Network":[50],"(FEGO_CBNet)-based":[51],"approach":[52],"for":[53],"robust":[54],"road":[55,120],"anomaly":[56,121],"detection.":[57],"Initially,":[58],"the":[59,62,76,85,88,100,110,119,126,129,134,173,178],"frames":[60,68],"from":[61],"video":[63],"are":[64,113,169,187],"extracted.":[65,114],"Pre-processing":[66],"of":[67,91,103,136,175,181],"achieved":[70],"by":[71,95],"Gaussian":[73],"Filter,":[74],"where":[75,87],"noise":[77],"removed.":[79],"The":[80,115],"Exponential":[81,96],"Logarithmic":[82,97],"Loss-K-Net":[83],"segments":[84],"object,":[86],"loss":[89],"function":[90],"K-Net":[92],"modified":[94],"Loss.":[98],"Moreover,":[99],"Pyramid":[101],"histogram":[102],"oriented":[104],"gradients":[105],"(PHoG)":[106],"as":[107,109],"well":[108],"statistical":[111],"features":[112],"CBNet":[116],"carries":[117],"out":[118],"object":[122],"detection,":[123],"in":[124],"which":[125],"FEGO":[127,132],"trains":[128],"CBNet.":[130],"Here,":[131],"integration":[135],"Optimization":[138],"Algorithm":[139],"(FOA)":[140],"Grouper":[144],"Optimizer":[145],"(EGO).":[146],"addition,":[148],"Normalized":[149,154,157,164],"Mean":[150,159,165],"Square":[151,160],"Error":[152,161,167],"(MSE),":[153],"R-squared":[155],"(R2),":[156],"Root":[158],"(RMSE),":[162],"Absolute":[166],"(MAE)":[168],"used":[170],"measure":[172],"effectiveness":[174],"FEGO_CBNet,":[176],"corresponding":[179],"values":[180],"0.163,":[182],"0.219,":[183],"0.403,":[184],"0.120":[186],"achieved.":[188]},"counts_by_year":[],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2026-04-07T00:00:00"}