{"id":"https://openalex.org/W3089461950","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207603","title":"FPA-DNN: A Forward Propagation Acceleration based Deep Neural Network for Ship Detection","display_name":"FPA-DNN: A Forward Propagation Acceleration based Deep Neural Network for Ship Detection","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089461950","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207603","mag":"3089461950"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5009620077","display_name":"Feng Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Wang","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023185671","display_name":"Fanshu Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanshu Liao","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029420912","display_name":"Huiqing Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiqing Zhu","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009620077"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":0.3908,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.62079048,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983000159263611,"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":0.9983000159263611,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9890000224113464,"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":0.7635514736175537},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.7075104713439941},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6473113298416138},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6460185050964355},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6148263812065125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5660021305084229},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5653029680252075},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.52863609790802},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5283677577972412},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5106685757637024},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4707180857658386},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4499720335006714},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.387019544839859},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37576910853385925}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7635514736175537},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.7075104713439941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6473113298416138},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6460185050964355},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6148263812065125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5660021305084229},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5653029680252075},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.52863609790802},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5283677577972412},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5106685757637024},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4707180857658386},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4499720335006714},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.387019544839859},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37576910853385925},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207603","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207603","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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":43,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1836465849","https://openalex.org/W2100503224","https://openalex.org/W2102605133","https://openalex.org/W2106054500","https://openalex.org/W2109255472","https://openalex.org/W2114766824","https://openalex.org/W2276892413","https://openalex.org/W2313179380","https://openalex.org/W2370624258","https://openalex.org/W2531409750","https://openalex.org/W2534798644","https://openalex.org/W2588505606","https://openalex.org/W2612445135","https://openalex.org/W2613718673","https://openalex.org/W2619307294","https://openalex.org/W2632026579","https://openalex.org/W2735189290","https://openalex.org/W2747590145","https://openalex.org/W2773134966","https://openalex.org/W2798678112","https://openalex.org/W2886851211","https://openalex.org/W2949941638","https://openalex.org/W2962851801","https://openalex.org/W2962965870","https://openalex.org/W2963037989","https://openalex.org/W2963125010","https://openalex.org/W2963257831","https://openalex.org/W2964228333","https://openalex.org/W2970212220","https://openalex.org/W4297775537","https://openalex.org/W4302088125","https://openalex.org/W6638667902","https://openalex.org/W6677103964","https://openalex.org/W6694806693","https://openalex.org/W6726275242","https://openalex.org/W6734062232","https://openalex.org/W6737664043","https://openalex.org/W6738693370","https://openalex.org/W6743270556","https://openalex.org/W6746627391","https://openalex.org/W6753490951"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W3157543420"],"abstract_inverted_index":{"Ship":[0],"detection":[1,22,31,43,51,63,72,124,149,178,181,192,198],"in":[2,11,106],"optical":[3,155],"satellite":[4],"images":[5],"has":[6],"played":[7],"an":[8],"important":[9],"role":[10],"the":[12,29,40,50,60,71,76,92,95,108,117,123,131,140,148,154,170,174,177,185,191,197],"field":[13],"of":[14,35,42,62,94,119],"remote":[15,156],"sensing":[16,157],"for":[17],"a":[18,81,102],"long":[19],"time.":[20],"Many":[21],"methods":[23],"have":[24],"been":[25],"proposed":[26],"to":[27,49,68,90],"address":[28],"ship":[30,96],"problem,":[32],"and":[33,121,130,144,180,183,194],"most":[34],"them":[36],"mainly":[37],"focus":[38],"on":[39,75,153,176],"improvement":[41,61],"accuracy":[44,128,179,193],"but":[45,65],"rarely":[46],"pay":[47],"attention":[48],"speed.":[52],"In":[53],"this":[54],"paper,":[55],"we":[56,79],"not":[57],"only":[58],"consider":[59],"accuracy,":[64],"also":[66],"try":[67],"speed":[69,125,146,195],"up":[70,147,196],"process.":[73,150],"Based":[74],"YOLOv2":[77],"model,":[78,105],"propose":[80],"forward":[82,134],"propagation":[83,135],"acceleration-based":[84],"deep":[85,109,166],"neural":[86,110],"network":[87,111],"model":[88,100,112,172,187],"(FPA-DNN)":[89],"enhance":[91],"performance":[93],"detection.":[97],"The":[98],"FPA-DNN":[99,186],"is":[101],"hybrid":[103],"learning":[104,167],"which":[107],"LSDN":[113,171],"can":[114,138,188],"effectively":[115],"reduce":[116],"number":[118],"parameters":[120],"improve":[122,190],"with":[126,163],"no":[127],"loss,":[129],"pruning":[132],"based":[133],"acceleration":[136],"algorithm":[137],"remove":[139],"redundant":[141],"convolution":[142],"kernels":[143],"further":[145,189],"Experimental":[151],"results":[152],"image":[158],"dataset":[159],"show":[160],"that,":[161],"compared":[162],"several":[164],"state-of-the-art":[165],"models,":[168],"1)":[169],"outperforms":[173],"others":[175],"speed;":[182],"2)":[184],"process":[199],"significantly.":[200]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}