{"id":"https://openalex.org/W4414647042","doi":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174406","title":"WCANet: Wavelet-Based Cross-Attention Framework for Multispectral Pedestrian Detection in Intelligent Transportation Systems","display_name":"WCANet: Wavelet-Based Cross-Attention Framework for Multispectral Pedestrian Detection in Intelligent Transportation Systems","publication_year":2025,"publication_date":"2025-06-17","ids":{"openalex":"https://openalex.org/W4414647042","doi":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174406"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2025-spring65109.2025.11174406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100631506","display_name":"Sirui Wang","orcid":"https://orcid.org/0000-0001-9519-5741"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sirui Wang","raw_affiliation_strings":["College of Electronic Information and Optical Engineering Nankai University,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Optical Engineering Nankai University,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100342711","display_name":"Liang Dong","orcid":"https://orcid.org/0000-0002-3646-8452"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Dong","raw_affiliation_strings":["College of Electronic Information and Optical Engineering Nankai University,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Optical Engineering Nankai University,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037386810","display_name":"Haicheng Zhang","orcid":"https://orcid.org/0000-0002-5357-6332"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haicheng Zhang","raw_affiliation_strings":["College of Electronic Information and Optical Engineering Nankai University,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Optical Engineering Nankai University,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101701886","display_name":"Guiling Sun","orcid":"https://orcid.org/0000-0001-5283-1760"},"institutions":[{"id":"https://openalex.org/I205237279","display_name":"Nankai University","ror":"https://ror.org/01y1kjr75","country_code":"CN","type":"education","lineage":["https://openalex.org/I205237279"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiling Sun","raw_affiliation_strings":["College of Electronic Information and Optical Engineering Nankai University,Tianjin,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Information and Optical Engineering Nankai University,Tianjin,China","institution_ids":["https://openalex.org/I205237279"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205237279"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46638194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9571999907493591,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9571999907493591,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9549000263214111,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9534000158309937,"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/multispectral-image","display_name":"Multispectral image","score":0.858299970626831},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.8080999851226807},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6506999731063843},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5498999953269958},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5063999891281128},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4666000008583069},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4569000005722046},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4397999942302704},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.43549999594688416}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.858299970626831},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8080999851226807},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732200026512146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6812999844551086},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6506999731063843},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5625},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5498999953269958},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5063999891281128},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4569000005722046},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4397999942302704},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.43549999594688416},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.40639999508857727},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C104541649","wikidata":"https://www.wikidata.org/wiki/Q6935090","display_name":"Multispectral pattern recognition","level":3,"score":0.3506999909877777},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3440999984741211},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28519999980926514},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2851000130176544},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2849000096321106},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2651999890804291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2025-spring65109.2025.11174406","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174406","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3764941934","display_name":null,"funder_award_id":"24ZY-CGYS00680","funder_id":"https://openalex.org/F4320336756","funder_display_name":"Tianjin Science and Technology Program"},{"id":"https://openalex.org/G6661048481","display_name":null,"funder_award_id":"92473208","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336756","display_name":"Tianjin Science and Technology Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1910108985","https://openalex.org/W2031454541","https://openalex.org/W2929607865","https://openalex.org/W2963188557","https://openalex.org/W2987131085","https://openalex.org/W3116967329","https://openalex.org/W3186570689","https://openalex.org/W3213472242","https://openalex.org/W4220724622","https://openalex.org/W4321195219","https://openalex.org/W4360978940","https://openalex.org/W4385801610","https://openalex.org/W4386189887","https://openalex.org/W4388505307","https://openalex.org/W4392796741"],"related_works":[],"abstract_inverted_index":{"Fast,":[0],"accurate,":[1],"and":[2,34,46,54,82,137,157,165],"robust":[3],"pedestrian":[4,29,160],"detection":[5,30,161],"is":[6],"crucial":[7],"for":[8,37],"intelligent":[9],"transportation":[10],"systems.":[11],"To":[12],"address":[13],"the":[14,65,89,101],"limitations":[15],"of":[16],"single-modality":[17],"detectors":[18],"in":[19,117],"complex":[20],"traffic":[21],"scenarios,":[22],"we":[23,41,63,122],"propose":[24,64],"WCANet,":[25],"a":[26,43,59,124],"novel":[27],"multispectral":[28,143,159],"framework":[31,49],"integrating":[32],"visible":[33,53],"infrared":[35,55],"images":[36],"all-weather":[38],"monitoring.":[39],"Specifically,":[40],"design":[42,123],"frequency-domain":[44],"interaction":[45,81,104],"spatial-domain":[47],"fusion":[48,130],"to":[50,76,112,140],"integrate":[51],"multi-level":[52],"features":[56],"extracted":[57],"by":[58],"dual-stream":[60],"network.":[61],"First,":[62],"Wavelet-based":[66],"Cross-Attention":[67,92],"(WCA)":[68],"module,":[69,94,131],"which":[70,95],"employs":[71],"discrete":[72],"wavelet":[73],"transform":[74],"(DWT)":[75],"decouple":[77],"low-frequency":[78,102],"homogeneous":[79],"feature":[80,85,103,129,144],"high-frequency":[83,98,119],"complementary":[84],"preservation.":[86],"We":[87],"develop":[88],"Iterative":[90],"High-Low":[91],"(I-HLCA)":[93],"innovatively":[96],"introduces":[97],"attention":[99],"into":[100],"process.":[105],"Additionally,":[106],"multi-head":[107],"dilated":[108],"convolutions":[109],"are":[110],"used":[111],"explore":[113],"local":[114],"spatial":[115,128,138],"similarities":[116],"cross-modality":[118],"features.":[120],"Furthermore,":[121],"channel":[125],"attention-guided":[126],"visible-infrared":[127],"enabling":[132],"joint":[133],"optimization":[134],"across":[135],"frequency":[136],"domains":[139],"comprehensively":[141],"enhance":[142],"representation.":[145],"Comprehensive":[146],"experiments":[147],"demonstrate":[148],"that":[149],"WCANet":[150],"achieves":[151],"state-of-the-art":[152],"performance":[153],"on":[154],"both":[155],"KAIST":[156],"LLVIP":[158],"benchmarks,":[162],"delivering":[163],"stable":[164],"reliable":[166],"detection.":[167]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
