{"id":"https://openalex.org/W3080319475","doi":"https://doi.org/10.1109/access.2020.3018306","title":"DA-Net: Pedestrian Detection Using Dense Connected Block and Attention Modules","display_name":"DA-Net: Pedestrian Detection Using Dense Connected Block and Attention Modules","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3080319475","doi":"https://doi.org/10.1109/access.2020.3018306","mag":"3080319475"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3018306","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3018306","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09172072.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09172072.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041523465","display_name":"Ruihong Yin","orcid":"https://orcid.org/0000-0001-5989-8976"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruihong Yin","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5989-8976","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102754346","display_name":"Rufei Zhang","orcid":"https://orcid.org/0000-0002-8735-3655"},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rufei Zhang","raw_affiliation_strings":["Beijing Institute of Control and Electronics Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8735-3655","affiliations":[{"raw_affiliation_string":"Beijing Institute of Control and Electronics Technology, Beijing, China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067671443","display_name":"Wei Zhao","orcid":"https://orcid.org/0000-0002-6060-1022"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhao","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6060-1022","affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101773676","display_name":"Feng Jiang","orcid":"https://orcid.org/0000-0001-8817-6657"},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Jiang","raw_affiliation_strings":["Beijing Institute of Control and Electronics Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8817-6657","affiliations":[{"raw_affiliation_string":"Beijing Institute of Control and Electronics Technology, Beijing, China","institution_ids":["https://openalex.org/I202334528"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.5873,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.69336422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"153929","last_page":"153940"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.71391761302948},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6869778037071228},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6811578273773193},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.63686203956604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3931733965873718},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09248155355453491},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.08565807342529297},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08062192797660828}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.71391761302948},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6869778037071228},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6811578273773193},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.63686203956604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3931733965873718},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09248155355453491},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08565807342529297},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08062192797660828},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3018306","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3018306","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09172072.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:822a88376bd5471d9ae27c096ab71fa0","is_oa":true,"landing_page_url":"https://doaj.org/article/822a88376bd5471d9ae27c096ab71fa0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 153929-153940 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3018306","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3018306","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09172072.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.75,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3080319475.pdf","grobid_xml":"https://content.openalex.org/works/W3080319475.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1836465849","https://openalex.org/W1986905809","https://openalex.org/W2031454541","https://openalex.org/W2108598243","https://openalex.org/W2144506857","https://openalex.org/W2150066425","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2183182206","https://openalex.org/W2194775991","https://openalex.org/W2200528286","https://openalex.org/W2288122362","https://openalex.org/W2302502886","https://openalex.org/W2340897893","https://openalex.org/W2490270993","https://openalex.org/W2531915888","https://openalex.org/W2548197316","https://openalex.org/W2565639579","https://openalex.org/W2579985080","https://openalex.org/W2594507094","https://openalex.org/W2752782242","https://openalex.org/W2768489488","https://openalex.org/W2783808331","https://openalex.org/W2792824754","https://openalex.org/W2796347433","https://openalex.org/W2883363148","https://openalex.org/W2895451584","https://openalex.org/W2896540732","https://openalex.org/W2912631020","https://openalex.org/W2949117887","https://openalex.org/W2963037989","https://openalex.org/W2963091558","https://openalex.org/W2963150697","https://openalex.org/W2963300223","https://openalex.org/W2963403868","https://openalex.org/W2963404857","https://openalex.org/W2963420686","https://openalex.org/W2963681621","https://openalex.org/W3015440931","https://openalex.org/W3034638324","https://openalex.org/W3106250896","https://openalex.org/W4293584584","https://openalex.org/W4385245566","https://openalex.org/W6620707391","https://openalex.org/W6638667902","https://openalex.org/W6646904105","https://openalex.org/W6684191040","https://openalex.org/W6732243160","https://openalex.org/W6739901393","https://openalex.org/W6750227808","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W3122828758","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,33,82],"plays":[2],"an":[3,30],"important":[4],"role":[5],"in":[6,93,107,121,191],"some":[7],"areas":[8],"such":[9],"as":[10,109],"autonomous":[11],"driving,":[12],"but":[13,194],"due":[14],"to":[15,64,89,115,143],"heavy":[16],"occlusion":[17,189],"and":[18,59,74,150,167,172,180,207,229],"various":[19,72,86],"scales,":[20],"it":[21],"is":[22,78,182],"still":[23],"challenging.":[24],"In":[25,141],"this":[26],"article,":[27],"we":[28,96],"propose":[29],"improved":[31,216],"pedestrian":[32,94,192],"method":[34,214],"called":[35],"DA-Net":[36,46],"based":[37],"on":[38,85,205,225,231],"the":[39,65,81,99,117,134,176,226,232],"two-stage":[40],"detector":[41],"Feature":[42],"Pyramid":[43],"Network":[44],"(FPN).":[45],"adds":[47],"Dense":[48],"Connected":[49],"Block":[50],"(DCB),":[51],"a":[52,137],"combination":[53,177],"of":[54,83,105,129,136,147,158,178,223,235],"channel-wise":[55],"attention":[56,61],"module":[57,62],"(CWAM)":[58],"global":[60,165],"(GAM)":[63],"network.":[66],"FPN":[67,108],"can":[68,124,132],"produce":[69],"features":[70,159],"with":[71,102,160,188,218,239],"scales":[73],"semantic":[75,127],"information,":[76],"which":[77,131],"good":[79],"for":[80,170,186,196,200],"pedestrians":[84,149],"scales.":[87],"Due":[88],"many":[90],"small-scale":[91,171,201],"targets":[92,106],"detection,":[95,193],"only":[97,184],"regard":[98],"low":[100],"layers":[101,120],"enough":[103],"details":[104],"prediction":[110,119],"layers.":[111],"After":[112],"several":[113],"DCBs":[114],"deepen":[116],"network,":[118],"our":[122,212],"network":[123],"encode":[125],"richer":[126],"information":[128,166,199],"targets,":[130],"make":[133],"location":[135],"target":[138],"more":[139],"precisely.":[140],"order":[142],"highlight":[144],"visible":[145],"parts":[146],"occluded":[148,152,173],"ignore":[151],"parts,":[153],"CWAM":[154,179],"weights":[155],"each":[156],"channel":[157],"different":[161],"importance.":[162],"GAM":[163,181],"aggregates":[164],"long-range":[168],"dependencies":[169],"targets.":[174,202],"Thus,":[175],"not":[183],"beneficial":[185],"coping":[187],"problem":[190],"also":[195],"gaining":[197],"environmental":[198],"Evaluation":[203],"results":[204],"CUHK":[206,227],"CityPersons":[208,236],"datasets":[209],"show":[210],"that":[211],"proposed":[213],"achieves":[215],"performance":[217],"log-average":[219],"miss":[220],"rate":[221],"reduction":[222],"9.6%":[224],"dataset":[228,237],"6.1%":[230],"Heavy":[233],"subset":[234],"compared":[238],"FPN.":[240]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}