{"id":"https://openalex.org/W3215223528","doi":"https://doi.org/10.1145/3484274.3484283","title":"Improvement of Detection Rate for Small Objects Using Pre-processing Network","display_name":"Improvement of Detection Rate for Small Objects Using Pre-processing Network","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3215223528","doi":"https://doi.org/10.1145/3484274.3484283","mag":"3215223528"},"language":"en","primary_location":{"id":"doi:10.1145/3484274.3484283","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3484274.3484283","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Control and Computer Vision","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/A5017401902","display_name":"Doo Hee Lee","orcid":"https://orcid.org/0000-0001-9092-7173"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Doo Hee Lee","raw_affiliation_strings":["Visual Image Media Lab, Chung-Ang University, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visual Image Media Lab, Chung-Ang University, Republic of Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014449121","display_name":"Gi Soon","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gi Soon Cha","raw_affiliation_strings":["Visual Image Media Lab, Chung-Ang University, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visual Image Media Lab, Chung-Ang University, Republic of Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085193526","display_name":"Ehtesham Iqbal","orcid":"https://orcid.org/0000-0003-1798-036X"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ehtesham Iqbal","raw_affiliation_strings":["Visual Image Media Lab, Chung-Ang University, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visual Image Media Lab, Chung-Ang University, Republic of Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020338294","display_name":"Hyun Chul Song","orcid":null},"institutions":[{"id":"https://openalex.org/I129672417","display_name":"Namseoul University","ror":"https://ror.org/0509ndt57","country_code":"KR","type":"education","lineage":["https://openalex.org/I129672417"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Chul Song","raw_affiliation_strings":["Nam-Seoul University, Nam-Seoul University, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nam-Seoul University, Nam-Seoul University, Republic of Korea","institution_ids":["https://openalex.org/I129672417"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038058440","display_name":"Kwang Nam Choi","orcid":"https://orcid.org/0000-0002-7420-9216"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwang Nam Choi","raw_affiliation_strings":["Visual Image Media Lab, Chung-Ang University, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visual Image Media Lab, Chung-Ang University, Republic of Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15844778,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"50","last_page":"56"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9980000257492065,"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":0.9962999820709229,"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/computer-science","display_name":"Computer science","score":0.8321030735969543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6996675729751587},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6346344351768494},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5463091731071472},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5264506936073303},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.48679324984550476},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4362748861312866},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4294370710849762},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36565178632736206},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.22099569439888}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8321030735969543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6996675729751587},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6346344351768494},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5463091731071472},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5264506936073303},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.48679324984550476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4362748861312866},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4294370710849762},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36565178632736206},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.22099569439888},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3484274.3484283","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3484274.3484283","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Control and Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2107376108","https://openalex.org/W2766124895","https://openalex.org/W2766564705","https://openalex.org/W2848033333","https://openalex.org/W3019897735","https://openalex.org/W3080865364","https://openalex.org/W3131203858"],"related_works":["https://openalex.org/W2067317451","https://openalex.org/W4375867731","https://openalex.org/W2154771632","https://openalex.org/W2949096641","https://openalex.org/W4380075502","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W3210378990","https://openalex.org/W3034745255","https://openalex.org/W4254103348"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"(AI)":[2],"has":[3],"been":[4],"developing":[5],"in":[6,96,101,151],"a":[7,22,84],"variety":[8],"of":[9,30,38,126,133,139,148,163],"methods":[10],"over":[11],"the":[12,28,36,39,62,89,97,102,111,116,134,146,152,159,164,174,178],"past":[13],"decade.":[14],"However":[15],"most":[16],"AI":[17,31],"experts":[18],"worried":[19],"to":[20,87,144,167],"build":[21],"deep":[23,43,56,98],"or":[24,119],"wide":[25,45,58],"network":[26,100,112,157,166],"because":[27],"accuracy":[29],"models":[32],"depends":[33],"heavily":[34],"on":[35],"depth":[37],"network.":[40],"In":[41],"general":[42],"and":[44,57,59,70,78,131,141],"networks":[46,66,117],"are":[47,54,67],"better":[48],"at":[49],"learning":[50],"than":[51,114],"those":[52],"that":[53,173],"less":[55],"wide.":[60,120],"On":[61],"other":[63],"hand":[64],"deeper":[65,118],"more":[68],"complex":[69],"have":[71],"many":[72],"disadvantages":[73],"such":[74],"as":[75],"computational":[76],"cost":[77],"system":[79],"specification":[80],"dependency.":[81],"We":[82],"propose":[83],"novel":[85],"method":[86,106,176],"improve":[88],"average":[90,160],"recall":[91,161],"rate":[92,162],"for":[93],"small":[94,149,179],"objects":[95,150],"convolutional":[99],"paper.":[103],"The":[104,121,136,155],"proposed":[105,175],"added":[107],"pre-processing":[108,123,156],"layer":[109,124],"before":[110],"rather":[113],"stacking":[115],"presented":[122],"consists":[125],"two":[127],"major":[128],"parts:":[129],"up-sampling":[130,140],"down-sampling":[132,142],"data.":[135],"overall":[137],"objective":[138],"is":[143],"enhance":[145],"resolution":[147],"input":[153],"image.":[154],"improves":[158],"base":[165],"3.56%.":[168],"This":[169],"experiment":[170],"result":[171],"depicts":[172],"outperforms":[177],"object":[180],"detection":[181],"performance.":[182]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}