{"id":"https://openalex.org/W4411016083","doi":"https://doi.org/10.1145/3728725.3728732","title":"Improved Semantic Segmentation of Urban Road Scenes with Deep Dual-Resolution Network","display_name":"Improved Semantic Segmentation of Urban Road Scenes with Deep Dual-Resolution Network","publication_year":2025,"publication_date":"2025-02-21","ids":{"openalex":"https://openalex.org/W4411016083","doi":"https://doi.org/10.1145/3728725.3728732"},"language":"en","primary_location":{"id":"doi:10.1145/3728725.3728732","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3728725.3728732","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3728725.3728732","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3728725.3728732","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066782958","display_name":"Penggang Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I100188998","display_name":"Harbin University of Science and Technology","ror":"https://ror.org/04e6y1282","country_code":"CN","type":"education","lineage":["https://openalex.org/I100188998"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Penggang Guo","raw_affiliation_strings":["School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, China"],"raw_orcid":"https://orcid.org/0009-0005-6054-9278","affiliations":[{"raw_affiliation_string":"School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, China","institution_ids":["https://openalex.org/I100188998"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050793699","display_name":"Yunfan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfan Liu","raw_affiliation_strings":["School of Modern Post, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0009-0000-3842-4149","affiliations":[{"raw_affiliation_string":"School of Modern Post, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084744971","display_name":"Longfei Li","orcid":"https://orcid.org/0009-0007-2222-0230"},"institutions":[{"id":"https://openalex.org/I145897649","display_name":"Minzu University of China","ror":"https://ror.org/0044e2g62","country_code":"CN","type":"education","lineage":["https://openalex.org/I145897649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longfei Li","raw_affiliation_strings":["School of Information Engineering, Minzu University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-2222-0230","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Minzu University of China, Beijing, China","institution_ids":["https://openalex.org/I145897649"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042504465","display_name":"Changyuan Liu","orcid":"https://orcid.org/0000-0003-2204-0612"},"institutions":[{"id":"https://openalex.org/I100188998","display_name":"Harbin University of Science and Technology","ror":"https://ror.org/04e6y1282","country_code":"CN","type":"education","lineage":["https://openalex.org/I100188998"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changyuan Liu","raw_affiliation_strings":["School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, China"],"raw_orcid":"https://orcid.org/0000-0003-2204-0612","affiliations":[{"raw_affiliation_string":"School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin, Heilongjiang, China","institution_ids":["https://openalex.org/I100188998"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5066782958"],"corresponding_institution_ids":["https://openalex.org/I100188998"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10016688,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"37","last_page":"42"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9891999959945679,"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":0.9891999959945679,"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":0.9873999953269958,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.6901643872261047},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6240190267562866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6165115833282471},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6155573725700378},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5956321954727173},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4786161780357361},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4330449104309082}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6901643872261047},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6240190267562866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6165115833282471},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6155573725700378},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5956321954727173},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4786161780357361},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4330449104309082},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3728725.3728732","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3728725.3728732","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3728725.3728732","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3728725.3728732","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3728725.3728732","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3728725.3728732","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 2nd International Conference on Generative Artificial Intelligence and Information Security","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411016083.pdf","grobid_xml":"https://content.openalex.org/works/W4411016083.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W3036708415","https://openalex.org/W4287891172","https://openalex.org/W4390576780"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W4379231730","https://openalex.org/W2952466936","https://openalex.org/W1517180214","https://openalex.org/W4389858081","https://openalex.org/W2082780921","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W4236344264","https://openalex.org/W1522196789"],"abstract_inverted_index":{"In":[0],"response":[1],"to":[2,57,72,92,103],"the":[3,25,48,55,64,105,116,129,138,142],"requirements":[4],"for":[5],"efficiency":[6],"and":[7,97,158],"accuracy":[8,157],"in":[9,12],"segmentation":[10,21,143,156],"models":[11],"autonomous":[13],"driving":[14],"scenarios,":[15],"an":[16,133,152],"improved":[17],"urban":[18],"road":[19],"semantic":[20],"algorithm":[22],"based":[23,40],"on":[24,41,59,128],"Deep":[26,79],"Dual-Resolution":[27],"Network":[28],"(DDRNet)":[29],"has":[30],"been":[31],"proposed.":[32],"Firstly,":[33],"a":[34,78,120],"Channel":[35],"Residual":[36],"Attention":[37],"Module":[38,83],"(CRAM)":[39],"standard":[42],"attention":[43],"mechanisms":[44],"is":[45,90,101],"introduced":[46],"at":[47],"end":[49],"of":[50,54,107,126,135],"two":[51],"parallel":[52],"branches":[53],"DDRNet":[56],"focus":[58],"important":[60],"channel":[61],"features.":[62],"Secondly,":[63],"backbone":[65],"network":[66],"incorporates":[67],"Depthwise":[68],"Over-parameterized":[69],"Convolution":[70,88],"(DO-Conv)":[71],"boost":[73],"feature":[74,95],"extraction":[75],"performance.":[76],"Finally,":[77],"Aggregation":[80],"Pyramid":[81],"Pooling":[82],"(DAPPM)":[84],"with":[85],"Hybrid":[86],"Dilated":[87],"(HDC)":[89],"designed":[91],"capture":[93],"multi-scale":[94],"information,":[96],"Group":[98],"Normalization":[99],"(GN)":[100],"utilized":[102],"mitigate":[104],"impact":[106],"reduced":[108],"batch":[109],"size.":[110],"The":[111],"experimental":[112],"results":[113],"demonstrate":[114],"that":[115],"proposed":[117],"method":[118],"achieves":[119],"Mean":[121],"Intersection":[122],"over":[123,137],"Union":[124],"(MIoU)":[125],"77.0%":[127],"Cityscapes":[130],"dataset,":[131],"representing":[132],"improvement":[134],"1.1%":[136],"baseline":[139],"model.":[140],"Additionally,":[141],"speed":[144],"reaches":[145],"106":[146],"Frames":[147],"Per":[148],"Second":[149],"(FPS),":[150],"achieving":[151],"optimal":[153],"balance":[154],"between":[155],"computational":[159],"efficiency.":[160]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}