{"id":"https://openalex.org/W4406334864","doi":"https://doi.org/10.3390/sym17010111","title":"A Face Fatigue Detection Model for Edge Information Extraction","display_name":"A Face Fatigue Detection Model for Edge Information Extraction","publication_year":2025,"publication_date":"2025-01-13","ids":{"openalex":"https://openalex.org/W4406334864","doi":"https://doi.org/10.3390/sym17010111"},"language":"en","primary_location":{"id":"doi:10.3390/sym17010111","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010111","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/sym17010111","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023788657","display_name":"Ge Wang","orcid":"https://orcid.org/0009-0003-6816-372X"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Wang","raw_affiliation_strings":["College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"],"raw_orcid":"https://orcid.org/0009-0003-6816-372X","affiliations":[{"raw_affiliation_string":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016995348","display_name":"Fangqian Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fangqian Xu","raw_affiliation_strings":["College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"],"raw_orcid":"https://orcid.org/0009-0008-6000-6128","affiliations":[{"raw_affiliation_string":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenjie Sang","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjie Sang","raw_affiliation_strings":["College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033860961","display_name":"Yuteng Gao","orcid":"https://orcid.org/0000-0003-1398-7310"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuteng Gao","raw_affiliation_strings":["College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yue Han","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Han","raw_affiliation_strings":["College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409530","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0003-2922-3518"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["Technology Department, Shandong Xinhuaan Information Technology Co., Ltd., Qingdao 266041, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Department, Shandong Xinhuaan Information Technology Co., Ltd., Qingdao 266041, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5016995348"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":2.2835,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.8500012,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"17","issue":"1","first_page":"111","last_page":"111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7982308864593506},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.6806696653366089},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6439826488494873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5713263750076294},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.470164030790329},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4517086446285248},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36659860610961914},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33282268047332764},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3283214271068573},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29158803820610046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7982308864593506},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.6806696653366089},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6439826488494873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5713263750076294},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.470164030790329},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4517086446285248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36659860610961914},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33282268047332764},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3283214271068573},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29158803820610046},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/sym17010111","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010111","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fc3780f90bd3476ab6619fe95ee11985","is_oa":false,"landing_page_url":"https://doaj.org/article/fc3780f90bd3476ab6619fe95ee11985","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 17, Iss 1, p 111 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/sym17010111","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym17010111","pdf_url":null,"source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8600000143051147,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2193145675","https://openalex.org/W2510324307","https://openalex.org/W2752782242","https://openalex.org/W2946948417","https://openalex.org/W2963125010","https://openalex.org/W2963351448","https://openalex.org/W3002479972","https://openalex.org/W3096609285","https://openalex.org/W3100391843","https://openalex.org/W3106250896","https://openalex.org/W3122173535","https://openalex.org/W3124372372","https://openalex.org/W3166716987","https://openalex.org/W3169120473","https://openalex.org/W4366202073","https://openalex.org/W4366418136","https://openalex.org/W4386047745","https://openalex.org/W4388915117","https://openalex.org/W4390044342","https://openalex.org/W4390872550","https://openalex.org/W4394783332","https://openalex.org/W4401075461","https://openalex.org/W4401526458","https://openalex.org/W4403770406","https://openalex.org/W4403885794","https://openalex.org/W4404279832","https://openalex.org/W4404524866","https://openalex.org/W4404527584","https://openalex.org/W6762166850","https://openalex.org/W6864850783"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W4386075645","https://openalex.org/W4385574037","https://openalex.org/W4306309518"],"abstract_inverted_index":{"In":[0,22],"contemporary":[1],"society,":[2],"fatigue":[3,29,42],"driving":[4,20],"is":[5,46],"a":[6,27,132,137,143],"major":[7],"cause":[8],"of":[9,40,62,95,107,115,167],"traffic":[10],"accidents,":[11],"making":[12],"accurate":[13],"and":[14,38,53,142],"timely":[15],"detection":[16,30],"critical":[17,68],"for":[18,160],"improving":[19],"safety.":[21],"this":[23],"study,":[24],"we":[25],"propose":[26],"novel":[28],"model,":[31],"CSA-YOLO,":[32],"designed":[33],"to":[34,58,82,87,149],"enhance":[35,83],"the":[36,49,60,72,79,84,93,96,104,113,123,150,157,165],"accuracy":[37],"efficiency":[39],"facial":[41],"detection.":[43],"The":[44],"model":[45,52,126],"based":[47],"on":[48],"YOLOv9s":[50,152],"network":[51],"introduces":[54],"several":[55],"key":[56],"improvements":[57],"address":[59],"limitations":[61],"traditional":[63],"methods,":[64],"which":[65],"often":[66],"lose":[67],"edge":[69,89],"information.":[70],"First,":[71],"Cross-Stage":[73],"Partial":[74],"Network":[75],"(C3":[76],"module)":[77],"replaces":[78],"RepNCSPELAN4":[80],"module":[81],"model\u2019s":[85,158],"ability":[86],"extract":[88],"information":[90],"effectively.":[91],"Second,":[92],"incorporation":[94],"SimAM":[97],"attention":[98],"mechanism":[99],"improves":[100],"feature":[101],"selection,":[102],"while":[103],"Content-Aware":[105],"ReAssembly":[106],"Features":[108],"(CARAFE)":[109],"upsampling":[110],"operator":[111],"enhances":[112],"quality":[114],"reconstructed":[116],"image":[117],"details.":[118],"Experimental":[119],"results":[120,155],"demonstrate":[121],"that":[122],"proposed":[124],"CSA-YOLO":[125],"achieves":[127],"significant":[128],"performance":[129],"improvements,":[130],"with":[131],"2.24%":[133],"increase":[134],"in":[135,140,146,163],"accuracy,":[136],"2.58%":[138],"improvement":[139],"recall,":[141],"2.44%":[144],"boost":[145],"mAP":[147],"compared":[148],"original":[151],"model.":[153],"These":[154],"highlight":[156],"potential":[159],"practical":[161],"application":[162],"reducing":[164],"risks":[166],"fatigue-related":[168],"accidents.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}