{"id":"https://openalex.org/W7163074735","doi":"https://doi.org/10.48550/arxiv.2605.30939","title":"IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation","display_name":"IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163074735","doi":"https://doi.org/10.48550/arxiv.2605.30939"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30939","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.30939","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136676175","display_name":"Bingtao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bingtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130786976","display_name":"Daojie Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Daojie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137573049","display_name":"Fulong Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Fulong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137613198","display_name":"Jun Ma","orcid":"https://orcid.org/0000-0002-1137-9349"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137537052","display_name":"Liang Zhang","orcid":"https://orcid.org/0000-0003-3100-8593"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.6906999945640564,"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":0.6906999945640564,"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":0.18889999389648438,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.04500000178813934,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7660999894142151},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6489999890327454},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5724999904632568},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.439300000667572},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.40959998965263367},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4018000066280365},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3783000111579895}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7660999894142151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7067999839782715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819000244140625},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6489999890327454},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5781000256538391},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5724999904632568},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.40959998965263367},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4018000066280365},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.31220000982284546},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.30720001459121704},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2856999933719635},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30939","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30939","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8201720118522644}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Semantic":[0],"road":[1,59],"segmentation":[2,60],"is":[3],"important":[4],"for":[5,57],"autonomous":[6],"driving,":[7],"but":[8],"existing":[9,19],"methods":[10,22],"suffer":[11],"severe":[12],"performance":[13,115],"degradation":[14],"under":[15],"low-light":[16,84],"conditions.":[17,64,128],"Many":[18],"multi-modal":[20],"fusion":[21,56,68],"do":[23],"not":[24],"explicitly":[25],"adapt":[26],"to":[27],"illumination-dependent":[28],"changes":[29],"in":[30,152],"modality":[31],"reliability,":[32],"which":[33,144],"can":[34],"propagate":[35],"degraded":[36],"RGB":[37,71],"features":[38,74],"into":[39],"the":[40,76,117,138,141,146],"fused":[41],"representation":[42],"at":[43],"night.":[44],"We":[45,92],"propose":[46],"IAF-Net":[47],"(Illumination-Adaptive":[48],"Fusion":[49,79],"Network),":[50],"an":[51],"end-to-end":[52],"framework":[53],"with":[54,87],"illumination-adaptive":[55],"robust":[58],"across":[61,125],"different":[62],"lighting":[63],"It":[65],"dynamically":[66],"adjusts":[67],"weights":[69],"of":[70,140,150],"and":[72,82,103],"geometric":[73],"via":[75],"core":[77],"Illumination-Adaptive":[78],"(IAF)":[80],"module,":[81,143],"enhances":[83],"feature":[85],"selection":[86],"a":[88,132],"brightness-modulated":[89],"attention":[90],"decoder.":[91],"also":[93],"construct":[94],"two":[95],"dedicated":[96],"datasets:":[97],"nuScenes":[98],"Nighttime":[99],"Road":[100,106],"Segmentation":[101,107],"(nuScenes-NRS)":[102],"CARLA":[104],"Multi-Weather":[105],"(CARLA-MWRS).":[108],"Experiments":[109],"on":[110,131],"nuScenes-NRS":[111],"show":[112],"state-of-the-art":[113],"overall":[114],"among":[116],"compared":[118],"methods,":[119],"while":[120],"CARLA-MWRS":[121],"further":[122,136],"validates":[123],"robustness":[124],"adverse":[126],"weather":[127],"Ablation":[129],"studies":[130],"40%":[133],"training":[134],"subset":[135],"highlight":[137],"importance":[139],"IAF":[142],"provides":[145],"largest":[147],"individual":[148],"gain":[149],"0.70%":[151],"MaxF.":[153]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-02T00:00:00"}