{"id":"https://openalex.org/W2995643353","doi":"https://doi.org/10.1109/tencon.2019.8929376","title":"Semantic Scene Understanding in Unstructured Environment with Deep Convolutional Neural Network","display_name":"Semantic Scene Understanding in Unstructured Environment with Deep Convolutional Neural Network","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2995643353","doi":"https://doi.org/10.1109/tencon.2019.8929376","mag":"2995643353"},"language":"en","primary_location":{"id":"doi:10.1109/tencon.2019.8929376","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929376","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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/A5004972442","display_name":"Bhakti Baheti","orcid":"https://orcid.org/0000-0001-5475-3903"},"institutions":[{"id":"https://openalex.org/I4210093989","display_name":"Sanskriti Samvardhan Mandal","ror":"https://ror.org/01egfqj98","country_code":"IN","type":"other","lineage":["https://openalex.org/I4210093989"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Bhakti Baheti","raw_affiliation_strings":["Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, Maharashtra, India","institution_ids":["https://openalex.org/I4210093989"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047680817","display_name":"Suhas Gajre","orcid":"https://orcid.org/0000-0002-7021-3453"},"institutions":[{"id":"https://openalex.org/I4210093989","display_name":"Sanskriti Samvardhan Mandal","ror":"https://ror.org/01egfqj98","country_code":"IN","type":"other","lineage":["https://openalex.org/I4210093989"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Suhas Gajre","raw_affiliation_strings":["Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, Maharashtra, India","institution_ids":["https://openalex.org/I4210093989"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091593309","display_name":"Sanjay N. Talbar","orcid":"https://orcid.org/0000-0002-0111-4910"},"institutions":[{"id":"https://openalex.org/I4210093989","display_name":"Sanskriti Samvardhan Mandal","ror":"https://ror.org/01egfqj98","country_code":"IN","type":"other","lineage":["https://openalex.org/I4210093989"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjay Talbar","raw_affiliation_strings":["Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Center of Excellence in Signal and Image Processing, SGGS Institute of Engineering and Technology, Nanded, Maharashtra, India","institution_ids":["https://openalex.org/I4210093989"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004972442"],"corresponding_institution_ids":["https://openalex.org/I4210093989"],"apc_list":null,"apc_paid":null,"fwci":1.1233,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.82740836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"790","last_page":"795"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9972000122070312,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.8551840782165527},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7491374015808105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6117340326309204},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5560954213142395},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5014221668243408},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4795420169830322},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.46812477707862854},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4575035870075226},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4518711566925049},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.44887590408325195},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35696226358413696},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3227652907371521},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1402249038219452}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8551840782165527},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7491374015808105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6117340326309204},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5560954213142395},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5014221668243408},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4795420169830322},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.46812477707862854},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4575035870075226},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4518711566925049},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.44887590408325195},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35696226358413696},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3227652907371521},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1402249038219452},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon.2019.8929376","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon.2019.8929376","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)","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":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1745334888","https://openalex.org/W1923697677","https://openalex.org/W1948751323","https://openalex.org/W2194775991","https://openalex.org/W2340897893","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2630837129","https://openalex.org/W2762439315","https://openalex.org/W2901870313","https://openalex.org/W2962850830","https://openalex.org/W2963840672","https://openalex.org/W2963881378","https://openalex.org/W2964309882","https://openalex.org/W6640295612","https://openalex.org/W6640759395","https://openalex.org/W6696085341","https://openalex.org/W6739696289","https://openalex.org/W6748481559"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3112772842"],"abstract_inverted_index":{"Number":[0],"of":[1,67,81,109],"road":[2],"fatalities":[3],"have":[4,45,60],"been":[5,61],"continuously":[6],"increasing":[7],"since":[8],"last":[9],"few":[10],"decades":[11],"all":[12],"over":[13],"the":[14,26,55,107,113,117],"world.":[15],"Nowadays":[16],"advanced":[17],"driver":[18,27],"assistance":[19],"systems":[20],"are":[21,84],"being":[22],"developed":[23],"to":[24,94,105,138,142],"help":[25],"in":[28,49,63,70,86,101,112,145],"driving":[29],"process":[30],"and":[31],"semantic":[32,56,71],"scene":[33,114],"understanding":[34],"is":[35],"an":[36],"essential":[37],"task":[38],"for":[39],"it.":[40],"Convolutional":[41],"Neural":[42],"Networks":[43],"(CNN)":[44],"shown":[46],"impressive":[47],"progress":[48],"various":[50],"computer":[51],"vision":[52],"tasks":[53],"including":[54],"segmentation.":[57],"Various":[58],"architectures":[59],"proposed":[62],"literature":[64,146],"but":[65],"loss":[66],"spatial":[68],"acuity":[69],"segmentation":[72],"prevents":[73],"them":[74],"from":[75,131],"achieving":[76],"better":[77],"results":[78],"as":[79,99],"details":[80,108],"small":[82],"objects":[83,111],"lost":[85],"downsampling.":[87],"To":[88],"overcome":[89],"this":[90],"drawback,":[91],"we":[92],"propose":[93],"use":[95],"dilated":[96],"residual":[97],"network":[98],"backbone":[100],"DeepLabV3+":[102],"which":[103],"enables":[104],"preserve":[106],"smaller":[110],"without":[115],"reducing":[116],"receptive":[118],"field.":[119],"We":[120],"focus":[121],"our":[122],"work":[123],"on":[124],"India":[125],"Driving":[126],"dataset":[127],"(IDD)":[128],"containing":[129],"data":[130],"unstructured":[132],"traffic":[133],"scenario.":[134],"Proposed":[135],"architecture":[136],"proves":[137],"be":[139],"effective":[140],"compared":[141],"earlier":[143],"approaches":[144],"with":[147],"0.618":[148],"mIoU.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":6}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}