{"id":"https://openalex.org/W7027828162","doi":null,"title":"Design and implementation of a vehicle and pedestrian detection system based on deep learning","display_name":"Design and implementation of a vehicle and pedestrian detection system based on deep learning","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7027828162"},"language":"en","primary_location":{"id":"pmh:oai:lutpub.lut.fi:10024/169341","doi":null,"is_oa":true,"landing_page_url":"https://lutpub.lut.fi/handle/10024/169341","pdf_url":"https://lutpub.lut.fi/bitstream/10024/169341/1/bachelorsthesis_Ma_Jialu.pdf","source":{"id":"https://openalex.org/S4306402474","display_name":"LUTPub (LUT University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63548447","host_organization_name":"Lappeenranta-Lahti University of Technology","host_organization_lineage":["https://openalex.org/I63548447"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"fi=Kandidaatinty\u00f6|en=Bachelor's thesis|"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://lutpub.lut.fi/bitstream/10024/169341/1/bachelorsthesis_Ma_Jialu.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ma, Jialu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ma, Jialu","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":true,"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":true,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":9.999999747378752e-05,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.0,"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/vehicle-detection","display_name":"vehicle detection","score":0.996999979019165},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"pedestrian detection","score":1.0},{"id":"https://openalex.org/keywords/yolov8n","display_name":"YOLOv8n","score":0.9179999828338623},{"id":"https://openalex.org/keywords/biformer-attention","display_name":"BiFormer attention","score":0.7609999775886536},{"id":"https://openalex.org/keywords/small-object-detection","display_name":"small object detection","score":0.9430000185966492},{"id":"https://openalex.org/keywords/lightweight-object-detection-model","display_name":"lightweight object detection model","score":0.7360000014305115},{"id":"https://openalex.org/keywords/real-time-object-detection","display_name":"real-time object detection","score":0.7670000195503235},{"id":"https://openalex.org/keywords/intelligent-transportation-systems","display_name":"intelligent transportation systems","score":0.8730000257492065},{"id":"https://openalex.org/keywords/autonomous-driving","display_name":"autonomous driving","score":0.8600000143051147},{"id":"https://openalex.org/keywords/safety-monitoring","display_name":"safety monitoring","score":0.6800000071525574}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8258000016212463},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7975999712944031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7290999889373779},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.6391000151634216},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.638700008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6330999732017517},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4487000107765198},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C37789001","wikidata":"https://www.wikidata.org/wiki/Q782543","display_name":"Graphical user interface","level":2,"score":0.41040000319480896},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.398499995470047},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.37059998512268066},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.33070001006126404},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.328000009059906},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C98025372","wikidata":"https://www.wikidata.org/wiki/Q477538","display_name":"Systems architecture","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C99613125","wikidata":"https://www.wikidata.org/wiki/Q165194","display_name":"Application programming interface","level":2,"score":0.2800999879837036},{"id":"https://openalex.org/C89505385","wikidata":"https://www.wikidata.org/wiki/Q47146","display_name":"User interface","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2791999876499176},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.26249998807907104},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2587999999523163},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.25290000438690186},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:lutpub.lut.fi:10024/169341","doi":null,"is_oa":true,"landing_page_url":"https://lutpub.lut.fi/handle/10024/169341","pdf_url":"https://lutpub.lut.fi/bitstream/10024/169341/1/bachelorsthesis_Ma_Jialu.pdf","source":{"id":"https://openalex.org/S4306402474","display_name":"LUTPub (LUT University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63548447","host_organization_name":"Lappeenranta-Lahti University of Technology","host_organization_lineage":["https://openalex.org/I63548447"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"fi=Kandidaatinty\u00f6|en=Bachelor's thesis|"}],"best_oa_location":{"id":"pmh:oai:lutpub.lut.fi:10024/169341","doi":null,"is_oa":true,"landing_page_url":"https://lutpub.lut.fi/handle/10024/169341","pdf_url":"https://lutpub.lut.fi/bitstream/10024/169341/1/bachelorsthesis_Ma_Jialu.pdf","source":{"id":"https://openalex.org/S4306402474","display_name":"LUTPub (LUT University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63548447","host_organization_name":"Lappeenranta-Lahti University of Technology","host_organization_lineage":["https://openalex.org/I63548447"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"fi=Kandidaatinty\u00f6|en=Bachelor's thesis|"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5142999887466431,"id":"https://metadata.un.org/sdg/11"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7027828162.pdf","grobid_xml":"https://content.openalex.org/works/W7027828162.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1483870316","https://openalex.org/W2112796928","https://openalex.org/W2150066425","https://openalex.org/W2161969291","https://openalex.org/W2164598857","https://openalex.org/W2183877882","https://openalex.org/W2193145675","https://openalex.org/W2796347433","https://openalex.org/W2884585870","https://openalex.org/W2997192890","https://openalex.org/W3002210514","https://openalex.org/W3018757597","https://openalex.org/W3121696187","https://openalex.org/W3177052299","https://openalex.org/W3213105003","https://openalex.org/W4220832258","https://openalex.org/W4288871905","https://openalex.org/W4381488337","https://openalex.org/W4386075524","https://openalex.org/W4393142397","https://openalex.org/W4394858325","https://openalex.org/W4399237955","https://openalex.org/W4404959185","https://openalex.org/W4408367867"],"related_works":["https://openalex.org/W4411211673","https://openalex.org/W7221305052","https://openalex.org/W4399199002","https://openalex.org/W7217978393","https://openalex.org/W7151364852","https://openalex.org/W7117421768","https://openalex.org/W7140213766","https://openalex.org/W7219253900","https://openalex.org/W4410228655","https://openalex.org/W2944695781"],"abstract_inverted_index":{"With":[0],"the":[1,25,31,35,89,123,138],"rapid":[2],"development":[3],"of":[4,34,60,158],"deep":[5,8,49],"learning,":[6],"using":[7,143],"learning":[9],"to":[10,44,102,189],"achieve":[11],"efficient":[12],"and":[13,16,46,52,73,93,120,133,147,161,174,205],"accurate":[14],"vehicle":[15,51,132],"pedestrian":[17,53,134],"detection":[18,36,54,68,100,115,129,157,171,186],"has":[19,193],"become":[20],"a":[21,48,66,74,94,151,182],"research":[22],"hotspot":[23],"in":[24,38,106,131,197],"intelligent":[26,201],"transportation":[27],"field.":[28],"To":[29],"improve":[30],"application":[32,139,195],"performance":[33,130],"system":[37,57,83,180],"practical":[39],"scenarios,":[40],"this":[41],"thesis":[42],"aims":[43],"design":[45],"implement":[47],"learning-based":[50],"system.":[55],"The":[56,82],"architecture":[58],"consists":[59],"two":[61],"main":[62],"components":[63],"that":[64,154],"are":[65],"lightweight":[67],"model":[69,86,126,187],"optimized":[70,124],"by":[71,87],"YOLOv8n":[72,85],"graphical":[75],"user":[76],"interface":[77,153,190],"(GUI)":[78],"for":[79],"real-time":[80,162,168],"interaction.":[81],"optimizes":[84],"adding":[88],"BiFormer":[90],"attention":[91],"mechanism":[92],"160":[95,97],"\u00d7":[96],"small-scale":[98],"object":[99,114,156],"head":[101],"recognize":[103],"small":[104],"objects":[105],"complex":[107],"environments.":[108],"Through":[109],"comparative":[110],"experiments":[111],"with":[112],"classic":[113],"models":[116],"such":[117,199],"as":[118,200],"SSD":[119],"Faster":[121],"R-CNN,":[122],"YOLOv8":[125],"achieves":[127],"superior":[128],"detection.":[135],"In":[136],"addition,":[137],"layer":[140],"is":[141],"developed":[142],"Python":[144],"programming":[145],"language":[146],"PyQt5":[148],"framework,":[149],"providing":[150],"user-friendly":[152],"supports":[155],"images,":[159],"videos,":[160],"camera":[163],"inputs.":[164],"It":[165,192],"also":[166],"includes":[167],"statistical":[169],"analysis,":[170],"confidence":[172],"display,":[173],"category":[175],"distribution":[176],"visualization":[177],"functions.":[178],"This":[179],"provides":[181],"complete":[183],"solution":[184],"from":[185],"optimization":[188],"development.":[191],"good":[194],"potential":[196],"scenarios":[198],"transportation,":[202],"autonomous":[203],"driving,":[204],"safety":[206],"monitoring.":[207]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
