{"id":"https://openalex.org/W4313169528","doi":"https://doi.org/10.1109/icarm54641.2022.9959713","title":"Context-aware Method for Small Object Segmentation in Road Scenes","display_name":"Context-aware Method for Small Object Segmentation in Road Scenes","publication_year":2022,"publication_date":"2022-07-09","ids":{"openalex":"https://openalex.org/W4313169528","doi":"https://doi.org/10.1109/icarm54641.2022.9959713"},"language":"en","primary_location":{"id":"doi:10.1109/icarm54641.2022.9959713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarm54641.2022.9959713","pdf_url":null,"source":{"id":"https://openalex.org/S4363608069","display_name":"2022 International Conference on Advanced Robotics and Mechatronics (ICARM)","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 International Conference on Advanced Robotics and Mechatronics (ICARM)","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/A5100457230","display_name":"Haitao Wang","orcid":"https://orcid.org/0009-0004-2276-587X"},"institutions":[{"id":"https://openalex.org/I4210145005","display_name":"State Key Laboratory of Vehicle NVH and Safety Technology","ror":"https://ror.org/04e6h1p91","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145005"]},{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haitao Wang","raw_affiliation_strings":["State Key Laboratory of Vehicle NVH and Safety Technology,Chongqing,China,401122","School of Automotive Studies, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Vehicle NVH and Safety Technology,Chongqing,China,401122","institution_ids":["https://openalex.org/I4210145005"]},{"raw_affiliation_string":"School of Automotive Studies, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100323037","display_name":"Guang Chen","orcid":"https://orcid.org/0000-0002-7416-592X"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]},{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN","DE"],"is_corresponding":false,"raw_author_name":"Guang Chen","raw_affiliation_strings":["Tongji University,School of Automotive Studies,Shanghai,China,200092","Department of Informatics, Technical University of Munich, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Automotive Studies,Shanghai,China,200092","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Department of Informatics, Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100450024","display_name":"Zhijun Li","orcid":"https://orcid.org/0000-0002-3909-488X"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijun Li","raw_affiliation_strings":["University of Science and Technology of China,Department of Automation,China","Department of Automation, University of Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China,Department of Automation,China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"Department of Automation, University of Science and Technology of China, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005349483","display_name":"Jianyi Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianyi Peng","raw_affiliation_strings":["Tongji University,School of Automotive Studies,Shanghai,China,200092"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Automotive Studies,Shanghai,China,200092","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021887616","display_name":"Zhengfa Liu","orcid":"https://orcid.org/0000-0003-3544-5694"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengfa Liu","raw_affiliation_strings":["Tongji University,School of Automotive Studies,Shanghai,China,200092"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Automotive Studies,Shanghai,China,200092","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082689644","display_name":"Ya Wu","orcid":"https://orcid.org/0000-0002-5376-4448"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ya Wu","raw_affiliation_strings":["Tongji University,School of Automotive Studies,Shanghai,China,200092"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Automotive Studies,Shanghai,China,200092","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100457230"],"corresponding_institution_ids":["https://openalex.org/I116953780","https://openalex.org/I4210145005"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16790247,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":null,"first_page":"238","last_page":"243"},"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9950000047683716,"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":0.9918000102043152,"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/computer-science","display_name":"Computer science","score":0.7819170951843262},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7099103927612305},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6995092630386353},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6328079104423523},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5234476923942566},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47884368896484375},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4727362394332886},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42556232213974},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09058353304862976}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7819170951843262},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7099103927612305},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6995092630386353},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6328079104423523},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5234476923942566},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47884368896484375},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4727362394332886},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42556232213974},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09058353304862976},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarm54641.2022.9959713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarm54641.2022.9959713","pdf_url":null,"source":{"id":"https://openalex.org/S4363608069","display_name":"2022 International Conference on Advanced Robotics and Mechatronics (ICARM)","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 International Conference on Advanced Robotics and Mechatronics (ICARM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326971","display_name":"State Key Laboratory of Vehicle NVH and Safety Technology","ror":"https://ror.org/04e6h1p91"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1923697677","https://openalex.org/W2100588357","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2630837129","https://openalex.org/W2737258237","https://openalex.org/W2911964244","https://openalex.org/W2963881378","https://openalex.org/W2964350391","https://openalex.org/W3014641072","https://openalex.org/W3017247368","https://openalex.org/W3042916480","https://openalex.org/W3170841864","https://openalex.org/W3200444035","https://openalex.org/W3204079132","https://openalex.org/W3209945042","https://openalex.org/W4229443634","https://openalex.org/W4288325606","https://openalex.org/W4385245566","https://openalex.org/W6639824700","https://openalex.org/W6640295612","https://openalex.org/W6739696289","https://openalex.org/W6739901393","https://openalex.org/W6764322716","https://openalex.org/W6767071366","https://openalex.org/W6780772654"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W2628861693","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3215138031","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Thanks":[0],"to":[1,16,56,64,112,125,138,155],"the":[2,23,40,178],"rapid":[3],"development":[4],"of":[5,42,47,180],"deep":[6,88],"learning":[7],"algorithms,":[8],"autonomous":[9],"driving":[10],"perception":[11,24,41],"systems":[12],"are":[13,165],"now":[14],"able":[15],"handle":[17],"most":[18],"common":[19],"road":[20,44,79,117],"scenes.":[21],"But":[22],"in":[25,60,78,183],"some":[26],"corner":[27],"scenes":[28],"is":[29,53,136],"still":[30],"a":[31,71,84,94],"great":[32],"challenge.":[33],"In":[34,62],"this":[35,66],"paper,":[36],"we":[37,68,92],"focus":[38],"on":[39,167],"small":[43,48,75,114,185],"objects.":[45,186],"Perception":[46],"objects":[49],"at":[50],"long":[51],"distance":[52],"diffcult":[54],"due":[55],"their":[57],"low":[58],"resolution":[59],"images.":[61],"order":[63],"address":[65],"issue,":[67],"firstly":[69],"build":[70],"large":[72],"dataset":[73,170],"for":[74,87],"object":[76],"segmentation":[77,173],"scenes,":[80],"which":[81,175],"could":[82],"be":[83],"data":[85],"support":[86],"learning-based":[89],"method.":[90],"Then,":[91],"propose":[93],"method":[95,182],"that":[96],"contains":[97],"three":[98],"modules:":[99],"transformer-based":[100,120],"attention":[101,121],"module,":[102],"feature":[103],"fusion":[104,134],"module":[105,111,122,135,147],"and":[106,127,142,171],"atrous":[107,149],"convolution-based":[108],"auxiliary":[109],"loss":[110,146],"divide":[113],"obstacles":[115],"from":[116],"area.":[118],"The":[119],"converts":[123],"images":[124],"sequences":[126],"exploits":[128],"global":[129],"context":[130,157],"information":[131,141,158],"adequately.":[132],"Feature":[133],"designed":[137],"combine":[139],"texture":[140],"semantic":[143],"information.":[144],"Auxiliary":[145],"takes":[148],"convolution":[150],"with":[151],"multiple":[152],"dilation":[153],"rates":[154],"catch":[156],"as":[159,161],"much":[160],"possible.":[162],"Finally,":[163],"experiments":[164],"conducted":[166],"both":[168],"self-collected":[169],"generic":[172],"dataset,":[174],"all":[176],"show":[177],"superiority":[179],"our":[181],"segmenting":[184]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}