{"id":"https://openalex.org/W3203672534","doi":"https://doi.org/10.48550/arxiv.2106.07015","title":"Siamese Network Training Using Artificial Triplets By Sampling and Image Transformation","display_name":"Siamese Network Training Using Artificial Triplets By Sampling and Image Transformation","publication_year":2021,"publication_date":"2021-06-13","ids":{"openalex":"https://openalex.org/W3203672534","doi":"https://doi.org/10.48550/arxiv.2106.07015","mag":"3203672534"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2106.07015","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.07015","pdf_url":"https://arxiv.org/pdf/2106.07015","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2106.07015","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035308242","display_name":"Ammar N. Abbas","orcid":"https://orcid.org/0000-0002-2578-5137","observed_orcids":["https://orcid.org/0000-0002-2578-5137"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abbas, Ammar N.","raw_affiliation_strings":["BSB Artificial Intelligence - OSCAR Collision Avoidance System for Boats Linz , Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BSB Artificial Intelligence - OSCAR Collision Avoidance System for Boats Linz , Austria","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Moser, David","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moser, David","raw_affiliation_strings":["BSB Artificial Intelligence - OSCAR Collision Avoidance System for Boats Linz , Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BSB Artificial Intelligence - OSCAR Collision Avoidance System for Boats Linz , Austria","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7128999829292297,"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":0.7128999829292297,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.25949999690055847,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.007400000002235174,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/siamese-network","display_name":"Siamese network","score":0.8920000195503235},{"id":"https://openalex.org/keywords/triplet-loss","display_name":"triplet loss","score":0.6050000190734863},{"id":"https://openalex.org/keywords/image-transformation","display_name":"image transformation","score":0.6309999823570251},{"id":"https://openalex.org/keywords/infrared-thermography","display_name":"infrared thermography","score":0.5120000243186951},{"id":"https://openalex.org/keywords/underwater-object-detection","display_name":"underwater object detection","score":0.753000020980835},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"obstacle avoidance","score":0.6029999852180481},{"id":"https://openalex.org/keywords/real-time-object-tracking","display_name":"real-time object tracking","score":0.8169999718666077},{"id":"https://openalex.org/keywords/collision-prediction","display_name":"collision prediction","score":0.6919999718666077},{"id":"https://openalex.org/keywords/annotation-tools","display_name":"annotation tools","score":0.47999998927116394},{"id":"https://openalex.org/keywords/autonomous-boats","display_name":"autonomous boats","score":0.3499999940395355}],"concepts":[{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.7176376581192017},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.7144389748573303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6324520111083984},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6001800894737244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5850886702537537},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.570972740650177},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44193005561828613},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.422685444355011},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38684535026550293},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.34464073181152344},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11223268508911133},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0997914969921112},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2106.07015","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.07015","pdf_url":"https://arxiv.org/pdf/2106.07015","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2106.07015","doi":"https://doi.org/10.48550/arxiv.2106.07015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2106.07015","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2106.07015","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2106.07015","pdf_url":"https://arxiv.org/pdf/2106.07015","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3203672534.pdf","grobid_xml":"https://content.openalex.org/works/W3203672534.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W292488259","https://openalex.org/W1857884451","https://openalex.org/W1941385496","https://openalex.org/W1971419089","https://openalex.org/W1995903777","https://openalex.org/W1997651421","https://openalex.org/W2017343233","https://openalex.org/W2089961441","https://openalex.org/W2096590953","https://openalex.org/W2100917613","https://openalex.org/W2222512263","https://openalex.org/W2291627510","https://openalex.org/W2340000481","https://openalex.org/W2402144811","https://openalex.org/W2522785081","https://openalex.org/W2603203130","https://openalex.org/W2799861614","https://openalex.org/W2888456413","https://openalex.org/W2894176037","https://openalex.org/W2950703532","https://openalex.org/W2996118475","https://openalex.org/W3031754110"],"related_works":["https://openalex.org/W3171979602","https://openalex.org/W3028108509","https://openalex.org/W2762157407","https://openalex.org/W3047495616","https://openalex.org/W6941386032","https://openalex.org/W2625728734","https://openalex.org/W2914100082","https://openalex.org/W4313134476","https://openalex.org/W2903936898","https://openalex.org/W3045849401"],"abstract_inverted_index":{"The":[0],"device":[1],"used":[2,93],"in":[3,29,52,101],"this":[4],"work":[5,121],"detects":[6],"the":[7,10,13,21,27,32,41,50,75,97,105,120,127,132,137,141],"objects":[8,28,51],"over":[9],"surface":[11],"of":[12],"water":[14],"using":[15,94,112],"two":[16],"thermal":[17],"cameras":[18],"which":[19],"aid":[20],"users":[22],"to":[23,48,59,62],"detect":[24],"and":[25,54,134],"avoid":[26,40],"scenarios":[30],"where":[31],"human":[33],"eyes":[34],"cannot":[35],"(night,":[36],"fog,":[37],"etc.).":[38],"To":[39,103],"obstacle":[42],"collision":[43,72],"autonomously,":[44],"it":[45],"is":[46],"required":[47],"track":[49],"real-time":[53],"assign":[55],"a":[56,78,107],"specific":[57],"identity":[58],"each":[60],"object":[61],"determine":[63],"its":[64],"dynamics":[65],"(trajectory,":[66],"velocity,":[67],"etc.)":[68],"for":[69,83,122],"making":[70],"estimated":[71],"predictions.":[73],"In":[74],"following":[76],"work,":[77],"Machine":[79],"Learning":[80],"(ML)":[81],"approach":[82],"Computer":[84],"Vision":[85],"(CV)":[86],"called":[87],"Convolutional":[88],"Neural":[89],"Network":[90],"(CNN)":[91],"was":[92,110,117,129],"TensorFlow":[95],"as":[96],"high-level":[98],"programming":[99],"environment":[100],"Python.":[102],"validate":[104],"algorithm":[106,128],"test":[108,142],"set":[109],"generated":[111,139],"an":[113],"annotation":[114],"tool":[115],"that":[116],"created":[118],"during":[119],"proper":[123],"evaluation.":[124],"Once":[125],"validated,":[126],"deployed":[130],"on":[131],"platform":[133],"tested":[135],"with":[136],"sequence":[138],"by":[140],"boat.":[143]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2021-10-11T00:00:00"}