{"id":"https://openalex.org/W3034536179","doi":"https://doi.org/10.1109/access.2020.3000902","title":"An Accurate Object Detector With Effective Feature Extraction by Intrinsic Prior Knowledge","display_name":"An Accurate Object Detector With Effective Feature Extraction by Intrinsic Prior Knowledge","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3034536179","doi":"https://doi.org/10.1109/access.2020.3000902","mag":"3034536179"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3000902","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3000902","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09110867.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/ielx7/6287639/8948470/09110867.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046216872","display_name":"Maohui Li","orcid":"https://orcid.org/0000-0001-5657-9822"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Maohui Li","raw_affiliation_strings":["Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0001-5657-9822","affiliations":[{"raw_affiliation_string":"Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006957290","display_name":"Zhuoqun Fang","orcid":"https://orcid.org/0000-0003-1259-5470"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoqun Fang","raw_affiliation_strings":["Shenyang Aerospace University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0003-1259-5470","affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085638054","display_name":"Senxiang Lu","orcid":"https://orcid.org/0000-0002-7155-441X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senxiang Lu","raw_affiliation_strings":["Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-7155-441X","affiliations":[{"raw_affiliation_string":"Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046216872"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.2942,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55886591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"8","issue":null,"first_page":"130607","last_page":"130615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9976000189781189,"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":0.9973000288009644,"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/computer-science","display_name":"Computer science","score":0.8537189960479736},{"id":"https://openalex.org/keywords/pascal","display_name":"Pascal (unit)","score":0.8235485553741455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7114126682281494},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6989374160766602},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6150731444358826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5474640727043152},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5279820561408997},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48870041966438293},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4371928870677948},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.423240602016449},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4194294214248657},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41473889350891113}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8537189960479736},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.8235485553741455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7114126682281494},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6989374160766602},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6150731444358826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5474640727043152},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5279820561408997},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48870041966438293},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4371928870677948},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.423240602016449},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4194294214248657},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41473889350891113},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3000902","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3000902","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09110867.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:40ba8019d7d54e7d9555d84536a0ac15","is_oa":true,"landing_page_url":"https://doaj.org/article/40ba8019d7d54e7d9555d84536a0ac15","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 130607-130615 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3000902","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3000902","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09110867.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":[],"awards":[{"id":"https://openalex.org/G6818003529","display_name":null,"funder_award_id":"N180403021","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034536179.pdf","grobid_xml":"https://content.openalex.org/works/W3034536179.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2102605133","https://openalex.org/W2107634464","https://openalex.org/W2109255472","https://openalex.org/W2124386111","https://openalex.org/W2161969291","https://openalex.org/W2193045528","https://openalex.org/W2534457893","https://openalex.org/W2538008885","https://openalex.org/W2549139847","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2752782242","https://openalex.org/W2791697444","https://openalex.org/W2883989448","https://openalex.org/W2887280559","https://openalex.org/W2937795308","https://openalex.org/W2939411050","https://openalex.org/W2948818729","https://openalex.org/W2949338650","https://openalex.org/W2954300568","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963237621","https://openalex.org/W2963377935","https://openalex.org/W2963809521","https://openalex.org/W2963927307","https://openalex.org/W2964010755","https://openalex.org/W2964054038","https://openalex.org/W2964321964","https://openalex.org/W2964325361","https://openalex.org/W2965658867","https://openalex.org/W2981958729","https://openalex.org/W2989604896","https://openalex.org/W3025847014","https://openalex.org/W6620707391","https://openalex.org/W6631782140","https://openalex.org/W6761766065","https://openalex.org/W6763233178"],"related_works":["https://openalex.org/W4376620596","https://openalex.org/W3177249605","https://openalex.org/W2534152068","https://openalex.org/W3138508047","https://openalex.org/W1972515067","https://openalex.org/W4293054914","https://openalex.org/W1689909837","https://openalex.org/W4298525700","https://openalex.org/W2953362004","https://openalex.org/W2549121492"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1,169],"object":[2,198],"detection":[3,16,75,116,170,199],"algorithms":[4,17],"play":[5],"an":[6],"increasingly":[7],"important":[8],"role":[9],"in":[10,172],"many":[11],"computer":[12],"vision":[13],"tasks.":[14],"Many":[15],"are":[18,77,83,140],"trying":[19],"to":[20,26,34,62,85,88,102,154],"increase":[21,103],"the":[22,37,56,67,74,80,90,104,130,147,176,179,195,205],"depth":[23],"of":[24,44,59,93,107,132,197],"networks":[25,171],"improve":[27],"their":[28],"feature":[29,105],"expression":[30],"capacity.":[31],"However,":[32],"how":[33],"better":[35,165],"extract":[36],"essential":[38,91],"features":[39,82],"from":[40,185],"a":[41,96,114,143],"fixed":[42],"number":[43],"training":[45,108,206,210],"samples":[46,109],"is":[47,100,126,163,202],"often":[48],"ignored.":[49],"Thus,":[50],"we":[51],"propose":[52],"CoReSh":[53],"network,":[54,134],"using":[55],"prior":[57],"knowledge":[58],"intrinsic":[60,81,111],"features,":[61],"address":[63],"this":[64],"problem.":[65],"First,":[66],"relevant":[68],"optical":[69],"factors":[70],"which":[71],"may":[72],"affect":[73],"performance":[76,131],"explored,":[78],"and":[79,122,174,200],"proven":[84],"be":[86],"able":[87],"represent":[89],"information":[92,125],"objects.":[94],"Second,":[95],"new":[97],"data":[98],"augmentation":[99],"proposed":[101],"diversities":[106],"through":[110],"decomposition.":[112],"Third,":[113],"deep":[115,168],"neural":[117],"network":[118,162,180,191],"incorporating":[119],"color,":[120],"reflectance,":[121],"shading":[123],"image's":[124],"designed.":[127],"To":[128],"optimize":[129],"our":[133,161,190],"different":[135],"learning":[136],"rate":[137],"setting":[138],"tactics":[139],"compared.":[141],"Besides,":[142],"contrast":[144],"experiment":[145],"on":[146],"Pascal":[148],"VOC":[149],"dataset":[150],"was":[151],"set":[152],"up":[153],"verify":[155],"it.":[156],"The":[157],"result":[158],"shows":[159,188],"that":[160,189],"10%":[164],"than":[166],"ordinary":[167],"precision,":[173],"roughly":[175],"same":[177],"as":[178],"loaded":[181],"with":[182],"pre-training":[183],"weights":[184],"ILSVRC.":[186],"It":[187],"has":[192],"greatly":[193],"improved":[194],"precision":[196],"it":[201],"suitable":[203],"when":[204],"process":[207],"lacks":[208],"sufficient":[209],"samples.":[211]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}