{"id":"https://openalex.org/W2792351340","doi":"https://doi.org/10.1109/ipta.2017.8310098","title":"Comparison of CNN and MLP classifiers for algae detection in underwater pipelines","display_name":"Comparison of CNN and MLP classifiers for algae detection in underwater pipelines","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2792351340","doi":"https://doi.org/10.1109/ipta.2017.8310098","mag":"2792351340"},"language":"en","primary_location":{"id":"doi:10.1109/ipta.2017.8310098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2017.8310098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","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/A5065327460","display_name":"Edgar Iv\u00e1n S\u00e1nchez Medina","orcid":"https://orcid.org/0000-0001-5413-2795"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Edgar Medina","raw_affiliation_strings":["Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008767165","display_name":"Mariane R. Petraglia","orcid":"https://orcid.org/0000-0002-9451-3522"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mariane R. Petraglia","raw_affiliation_strings":["Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027382066","display_name":"Jos\u00e9 Gabriel R. C. Gomes","orcid":"https://orcid.org/0000-0003-3242-023X"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jose Gabriel R. C. Gomes","raw_affiliation_strings":["Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil","institution_ids":["https://openalex.org/I122140584"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103201626","display_name":"Antonio Petraglia","orcid":"https://orcid.org/0000-0001-8173-1283"},"institutions":[{"id":"https://openalex.org/I122140584","display_name":"Universidade Federal do Rio de Janeiro","ror":"https://ror.org/03490as77","country_code":"BR","type":"education","lineage":["https://openalex.org/I122140584"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Antonio Petraglia","raw_affiliation_strings":["Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Program Rio de Janeiro, Federal University of Rio de Janeiro-COPPE, Brazil","institution_ids":["https://openalex.org/I122140584"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065327460"],"corresponding_institution_ids":["https://openalex.org/I122140584"],"apc_list":null,"apc_paid":null,"fwci":1.5054,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.86027103,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.982699990272522,"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"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.982699990272522,"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"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9703999757766724,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7914555072784424},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7866053581237793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7856559753417969},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.671558678150177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6572879552841187},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.6285621523857117},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6013176441192627},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.5910083651542664},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5724307894706726},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5078974366188049},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4697307348251343},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4578579366207123},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.42062097787857056},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09077051281929016}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7914555072784424},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7866053581237793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7856559753417969},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.671558678150177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6572879552841187},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.6285621523857117},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6013176441192627},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.5910083651542664},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5724307894706726},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5078974366188049},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4697307348251343},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4578579366207123},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.42062097787857056},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09077051281929016},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ipta.2017.8310098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ipta.2017.8310098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","score":0.8100000023841858,"display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1591540418","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1927119114","https://openalex.org/W1982412139","https://openalex.org/W1991425210","https://openalex.org/W2007308255","https://openalex.org/W2021413089","https://openalex.org/W2102605133","https://openalex.org/W2111488376","https://openalex.org/W2115814941","https://openalex.org/W2120873659","https://openalex.org/W2155243719","https://openalex.org/W2163808566","https://openalex.org/W2288276606","https://openalex.org/W2311431599","https://openalex.org/W2543148038","https://openalex.org/W2564288455","https://openalex.org/W2594458601","https://openalex.org/W2594878708","https://openalex.org/W2962835968","https://openalex.org/W4293438950","https://openalex.org/W6637373629","https://openalex.org/W6639204139","https://openalex.org/W6640285205","https://openalex.org/W6652257021","https://openalex.org/W6696492154"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063","https://openalex.org/W2362514456","https://openalex.org/W2766585573","https://openalex.org/W4387490624","https://openalex.org/W4286643620"],"abstract_inverted_index":{"Artificial":[0],"neural":[1,18,22],"networks,":[2,19],"such":[3],"as":[4],"the":[5,115,120],"multilayer":[6],"perceptron":[7],"(MLP),":[8],"have":[9,25],"been":[10],"increasingly":[11],"employed":[12],"in":[13,39,61,80,108,126],"various":[14],"applications.":[15],"Recently,":[16],"deep":[17,51],"specially":[20],"convolutional":[21],"networks":[23],"(CNN),":[24],"received":[26],"considerable":[27],"attention":[28],"due":[29],"to":[30,33],"their":[31],"ability":[32],"extract":[34],"and":[35,53,82,91,103],"represent":[36],"high-level":[37],"abstractions":[38],"data":[40],"sets.":[41],"This":[42],"article":[43],"describes":[44],"a":[45,68,71,76,92],"vision":[46,55],"inspection":[47],"system":[48],"based":[49],"on":[50],"learning":[52],"computer":[54],"algorithms":[56],"for":[57],"detection":[58,89],"of":[59,87,98,122,130],"algae":[60],"underwater":[62],"pipelines.":[63],"The":[64,96],"proposed":[65],"algorithm":[66],"comprises":[67],"CNN":[69,104],"or":[70],"MLP":[72],"network,":[73],"followed":[74],"by":[75],"post-processing":[77,116],"stage":[78,117],"operating":[79],"spatial":[81],"temporal":[83],"domains,":[84],"employing":[85,100],"clustering":[86],"neighboring":[88],"positions":[90],"region":[93],"interception":[94],"framebuffer.":[95],"performances":[97],"MLP,":[99],"different":[101],"descriptors,":[102],"classifiers":[105],"are":[106],"compared":[107],"real-world":[109],"scenarios.":[110],"It":[111],"is":[112],"shown":[113],"that":[114],"considerably":[118],"decreases":[119],"number":[121],"false":[123],"positives,":[124],"resulting":[125],"an":[127],"accuracy":[128],"rate":[129],"99.39%.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}