{"id":"https://openalex.org/W4406892207","doi":"https://doi.org/10.1109/fllm63129.2024.10852440","title":"YOLO based Object Detection Enhancement based on Quadratic Convolution","display_name":"YOLO based Object Detection Enhancement based on Quadratic Convolution","publication_year":2024,"publication_date":"2024-11-26","ids":{"openalex":"https://openalex.org/W4406892207","doi":"https://doi.org/10.1109/fllm63129.2024.10852440"},"language":"en","primary_location":{"id":"doi:10.1109/fllm63129.2024.10852440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fllm63129.2024.10852440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Foundation and Large Language Models (FLLM)","raw_type":"proceedings-article"},"type":"article","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/A5001643572","display_name":"Abdesselam Ferdi","orcid":"https://orcid.org/0000-0002-7248-752X"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Abdesselam Ferdi","raw_affiliation_strings":["Constantine 1 - Frères Mentouri University Laboratoire LISSI Université Paris Est Créteil,Signal Processing Laboratory,Vitry Sur Seine,France,94400"],"affiliations":[{"raw_affiliation_string":"Constantine 1 - Frères Mentouri University Laboratoire LISSI Université Paris Est Créteil,Signal Processing Laboratory,Vitry Sur Seine,France,94400","institution_ids":["https://openalex.org/I2800365227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067068847","display_name":"Amir Nakib","orcid":"https://orcid.org/0000-0001-9620-9324"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Amir Nakib","raw_affiliation_strings":["Université Paris Est Créteil,Laboratoire LISSI,Vitry Sur Seine,France,94400"],"affiliations":[{"raw_affiliation_string":"Université Paris Est Créteil,Laboratoire LISSI,Vitry Sur Seine,France,94400","institution_ids":["https://openalex.org/I2800365227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001643572"],"corresponding_institution_ids":["https://openalex.org/I2800365227"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27470666,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"11","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.858299970626831,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.858299970626831,"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/T13579","display_name":"Image and Video Stabilization","score":0.850600004196167,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.833899974822998,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/convolution","display_name":"Convolution (computer science)","score":0.7514248490333557},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6351493000984192},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.6029618978500366},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5609884262084961},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5569809079170227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44959014654159546},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44801682233810425},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2907485365867615},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26420095562934875},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.04952695965766907}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7514248490333557},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6351493000984192},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.6029618978500366},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5609884262084961},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5569809079170227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44959014654159546},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44801682233810425},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2907485365867615},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26420095562934875},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.04952695965766907},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fllm63129.2024.10852440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fllm63129.2024.10852440","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Foundation and Large Language Models (FLLM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2737719445","https://openalex.org/W2898210368","https://openalex.org/W4387838477","https://openalex.org/W4239098401","https://openalex.org/W2067193074","https://openalex.org/W2182785089","https://openalex.org/W2382480268","https://openalex.org/W4312178642","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Object":[0],"detection":[1,53,151,169],"remains":[2],"a":[3,48,87,172],"critical":[4,83],"challenge":[5],"with":[6,25],"extensive":[7],"real-time":[8,56],"applications,":[9],"including":[10],"autonomous":[11],"vehicles,":[12],"medical":[13],"imaging,":[14],"and":[15,55,73],"surveillance":[16],"systems.":[17],"The":[18],"field":[19],"has":[20,45],"experienced":[21],"significant":[22,148],"progress,":[23],"particularly":[24],"the":[26,38,59,67,102,107,112,116,125,137,154,161],"advent":[27],"of":[28,62,115,153,163],"state-of-the-art":[29],"detectors":[30],"employing":[31],"convolutional":[32],"neural":[33],"network":[34],"architectures.":[35],"Among":[36],"these,":[37],"You":[39],"Only":[40],"Look":[41],"Once":[42],"(YOLO)":[43],"framework":[44],"emerged":[46],"as":[47],"benchmark,":[49],"excelling":[50],"in":[51,150,178],"balancing":[52],"accuracy":[54],"performance.":[57],"Nevertheless,":[58],"intrinsic":[60],"linearity":[61],"conventional":[63],"convolution":[64,99,108,123,165],"operations":[65],"constrains":[66],"network\u2019s":[68],"capacity":[69],"to":[70,130,166],"model":[71],"complex":[72],"hierarchical":[74],"data":[75],"representations.":[76],"In":[77],"this":[78,82,121],"work,":[79],"we":[80],"address":[81],"limitation":[84],"by":[85],"proposing":[86],"novel":[88],"nonlinear":[89],"operation":[90],"termed":[91],"quadratic":[92,98,122,164],"convolution.":[93],"Unlike":[94],"standard":[95,155],"linear":[96],"convolutions,":[97],"involves":[100],"squaring":[101],"input":[103],"image":[104],"elements":[105],"within":[106],"process,":[109],"thereby":[110],"augmenting":[111],"representational":[113],"power":[114],"feature":[117],"maps.":[118],"We":[119],"incorporated":[120],"into":[124],"latest":[126],"YOLOv8":[127,156],"detector":[128],"architecture":[129,181],"assess":[131],"its":[132],"effectiveness.":[133],"Experiments":[134],"conducted":[135],"on":[136],"widely":[138],"recognized":[139],"MS":[140],"COCO":[141],"dataset":[142],"indicate":[143],"that":[144],"our":[145],"approach":[146],"yields":[147],"improvements":[149],"performance":[152],"detector.":[157],"These":[158],"findings":[159],"underscore":[160],"potential":[162],"enhance":[167],"object":[168],"tasks,":[170],"offering":[171],"promising":[173],"direction":[174],"for":[175],"future":[176],"advancements":[177],"deep":[179],"learning":[180],"design.":[182]},"counts_by_year":[],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}