{"id":"https://openalex.org/W2551589584","doi":"https://doi.org/10.1109/avss.2016.7738076","title":"Fast CNN surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios","display_name":"Fast CNN surveillance pipeline for fine-grained vessel classification and detection in maritime scenarios","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2551589584","doi":"https://doi.org/10.1109/avss.2016.7738076","mag":"2551589584"},"language":"en","primary_location":{"id":"doi:10.1109/avss.2016.7738076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2016.7738076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","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/A5013545937","display_name":"Fouad Bousetouane","orcid":null},"institutions":[{"id":"https://openalex.org/I133999245","display_name":"University of Nevada, Las Vegas","ror":"https://ror.org/0406gha72","country_code":"US","type":"education","lineage":["https://openalex.org/I133999245"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fouad Bousetouane","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Nevada, Las Vegas, NV, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Nevada, Las Vegas, NV, USA","institution_ids":["https://openalex.org/I133999245"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048676969","display_name":"Brendan Morris","orcid":"https://orcid.org/0000-0002-8592-8806"},"institutions":[{"id":"https://openalex.org/I133999245","display_name":"University of Nevada, Las Vegas","ror":"https://ror.org/0406gha72","country_code":"US","type":"education","lineage":["https://openalex.org/I133999245"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brendan Morris","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Nevada, Las Vegas, NV, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Nevada, Las Vegas, NV, USA","institution_ids":["https://openalex.org/I133999245"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I133999245"],"apc_list":null,"apc_paid":null,"fwci":1.69,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.8969308,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"242","last_page":"248"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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":0.9987000226974487,"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/T11622","display_name":"Maritime Navigation and Safety","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11698","display_name":"Underwater Acoustics Research","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.842605710029602},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8182098865509033},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7355972528457642},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.7084084153175354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7083081603050232},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5877526998519897},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5712156295776367},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5539532899856567},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.524268627166748},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5210225582122803},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.431612491607666},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.42058634757995605}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.842605710029602},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8182098865509033},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7355972528457642},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.7084084153175354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7083081603050232},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5877526998519897},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5712156295776367},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5539532899856567},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.524268627166748},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5210225582122803},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.431612491607666},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.42058634757995605},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/avss.2016.7738076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2016.7738076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.6800000071525574}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1546411676","https://openalex.org/W1928906481","https://openalex.org/W1958328135","https://openalex.org/W1960182310","https://openalex.org/W2062118960","https://openalex.org/W2068730032","https://openalex.org/W2088049833","https://openalex.org/W2102605133","https://openalex.org/W2155893237","https://openalex.org/W2161969291","https://openalex.org/W2168356304","https://openalex.org/W2179352600","https://openalex.org/W2394595923","https://openalex.org/W2963542991","https://openalex.org/W3098722327","https://openalex.org/W6620707391","https://openalex.org/W6629368666","https://openalex.org/W6632670727","https://openalex.org/W6640376812","https://openalex.org/W6640833263"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W3037187668","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W2969228573"],"abstract_inverted_index":{"Deep":[0,86],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"proven":[6],"very":[7],"effective":[8],"for":[9,48,125],"many":[10],"vision":[11],"benchmarks":[12],"in":[13,32,53],"object":[14,24,79,126],"detection":[15,144],"and":[16,23,51,73,116,146],"classification":[17,52,110],"tasks.":[18],"However,":[19],"the":[20,62,90,100,131,156,167,172],"computational":[21],"complexity":[22],"resolution":[25],"requirements":[26],"of":[27,65,89,103,130,153,169],"CNNs":[28],"limit":[29],"their":[30],"applicability":[31],"wide-view":[33],"video":[34],"surveillance":[35,46],"settings":[36],"where":[37],"objects":[38],"are":[39,93],"small.":[40],"This":[41],"paper":[42],"presents":[43],"a":[44,104,117,162],"CNN":[45,87,114,139],"pipeline":[47,58,133],"vessel":[49],"localization":[50],"maritime":[54],"video.":[55],"The":[56,128,149],"proposed":[57,132,150],"is":[59,111,134],"build":[60],"upon":[61],"GPU":[63],"implementation":[64],"Fast-R-CNN":[66,159],"with":[67,95,122,136,141,158,161],"three":[68],"main":[69],"steps:(1)":[70],"Vessel":[71],"filtering":[72],"regions":[74,92],"proposal":[75],"using":[76,113],"low-cost":[77],"weak":[78],"detectors":[80],"based":[81],"on":[82,171],"hand-engineered":[83],"features.":[84],"(2)":[85],"features":[88,115],"candidates":[91],"computed":[94],"one":[96],"feed-forward":[97],"pass":[98],"from":[99],"high-level":[101],"layer":[102],"fine-tuned":[105],"VGG16":[106],"network.":[107],"(3)":[108],"Fine-grained":[109],"performed":[112],"support":[118],"vector":[119],"machine":[120],"classifier":[121],"linear":[123],"kernel":[124],"verification.":[127],"performance":[129],"compared":[135],"other":[137],"popular":[138],"architectures":[140],"respect":[142],"to":[143],"accuracy":[145],"evaluation":[147],"speed.":[148],"approach":[151],"mAP":[152],"61.10%":[154],"was":[155],"comparable":[157],"but":[160],"10\u00d7":[163],"speed":[164],"up":[165],"(on":[166],"order":[168],"Faster-R-CNN)":[170],"new":[173],"Annapolis":[174],"Maritime":[175],"Surveillance":[176],"Dataset.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
