{"id":"https://openalex.org/W2975178785","doi":"https://doi.org/10.2352/issn.2470-1173.2019.7.iriacv-458","title":"ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles","display_name":"ECDNet: Efficient Siamese Convolutional Network for Real-Time Small Object Change Detection from Ground Vehicles","publication_year":2019,"publication_date":"2019-01-13","ids":{"openalex":"https://openalex.org/W2975178785","doi":"https://doi.org/10.2352/issn.2470-1173.2019.7.iriacv-458","mag":"2975178785"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2019.7.iriacv-458","is_oa":true,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2019.7.iriacv-458","pdf_url":"https://library.imaging.org/ei/articles/31/7/art00010","source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://library.imaging.org/ei/articles/31/7/art00010","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076728626","display_name":"Sander R. Klomp","orcid":"https://orcid.org/0000-0002-0874-4720"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Sander R Klomp","raw_affiliation_strings":["Eindhoven, The Netherlands","With; Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands","Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven, The Netherlands","institution_ids":[]},{"raw_affiliation_string":"With; Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]},{"raw_affiliation_string":"Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032971361","display_name":"Dennis W. J. M. van de Wouw","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dennis W.J.M van de Wouw","raw_affiliation_strings":["Eindhoven, The Netherlands","With; Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands","Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven, The Netherlands","institution_ids":[]},{"raw_affiliation_string":"With; Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]},{"raw_affiliation_string":"Eindhoven University of Technology, SPS-VCA group of Electr. Eng.; Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008297198","display_name":"ViNotion B.V.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ViNotion B.V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5029008213","display_name":"Peter H. N. de With","orcid":"https://orcid.org/0000-0002-7639-7716"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peter H.N de With","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4866,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.78428082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":95},"biblio":{"volume":"31","issue":"7","first_page":"458","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11324","display_name":"Spectroscopy Techniques in Biomedical and Chemical Research","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9876000285148621,"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"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.984499990940094,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8118332624435425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7003288865089417},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6962348818778992},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.617357611656189},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6104623079299927},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5820194482803345},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.5406268835067749},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5314860343933105},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48455801606178284},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43956175446510315},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.4239177107810974},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41058778762817383},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1320929229259491}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8118332624435425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7003288865089417},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6962348818778992},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.617357611656189},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6104623079299927},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5820194482803345},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.5406268835067749},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5314860343933105},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48455801606178284},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43956175446510315},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.4239177107810974},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41058778762817383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1320929229259491},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.2352/issn.2470-1173.2019.7.iriacv-458","is_oa":true,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2019.7.iriacv-458","pdf_url":"https://library.imaging.org/ei/articles/31/7/art00010","source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/1084847e-a958-4a81-9a14-359b88f97da8","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/1084847e-a958-4a81-9a14-359b88f97da8","pdf_url":"https://pure.tue.nl/ws/files/137674161/s10.pdf","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Klomp, S, van de Wouw, D & de With, P 2019, ECDNet : Efficient siamese convolutional network for real-time small object change detection from ground vehicles. in Electronic Imaging : Intelligent Robotics and Industrial Applications using Computer Vision 2019., 458, IS&T, Electronic Imaging, Burlingame, California, United States, 13/01/19. https://doi.org/10.2352/ISSN.2470-1173.2019.7.IRIACV-458","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:914657","is_oa":false,"landing_page_url":"http://library.tue.nl/csp/dare/LinkToRepository.csp?recordnumber=914657","pdf_url":null,"source":{"id":"https://openalex.org/S4406923046","display_name":"TU/e Research Portal (Eindhoven University of Technology)","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"pmh:oai:library.tue.nl:914657","is_oa":false,"landing_page_url":"http://repository.tue.nl/914657","pdf_url":null,"source":{"id":"https://openalex.org/S4406923046","display_name":"TU/e Research Portal (Eindhoven University of Technology)","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"pmh:tue:oai:pure.tue.nl:publications/1084847e-a958-4a81-9a14-359b88f97da8","is_oa":true,"landing_page_url":"https://research.tue.nl/nl/publications/1084847e-a958-4a81-9a14-359b88f97da8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision 2019","raw_type":"info:eu-repo/semantics/conferencepaper"}],"best_oa_location":{"id":"doi:10.2352/issn.2470-1173.2019.7.iriacv-458","is_oa":true,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2019.7.iriacv-458","pdf_url":"https://library.imaging.org/ei/articles/31/7/art00010","source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2975178785.pdf","grobid_xml":"https://content.openalex.org/works/W2975178785.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W767484245","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1899804838","https://openalex.org/W1990613417","https://openalex.org/W2021354639","https://openalex.org/W2048482439","https://openalex.org/W2048658624","https://openalex.org/W2048737585","https://openalex.org/W2064499670","https://openalex.org/W2108598243","https://openalex.org/W2138621090","https://openalex.org/W2149933564","https://openalex.org/W2194775991","https://openalex.org/W2296059218","https://openalex.org/W2415309533","https://openalex.org/W2419597278","https://openalex.org/W2462670326","https://openalex.org/W2548511092","https://openalex.org/W2747510085","https://openalex.org/W2751993439","https://openalex.org/W2762439315","https://openalex.org/W2765295284","https://openalex.org/W2767778161","https://openalex.org/W2774991885","https://openalex.org/W2950094539","https://openalex.org/W4245456936","https://openalex.org/W4299518610","https://openalex.org/W6631190155","https://openalex.org/W6662452226","https://openalex.org/W6687483927","https://openalex.org/W6745899134"],"related_works":["https://openalex.org/W2599472179","https://openalex.org/W2568858292","https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W1515964938","https://openalex.org/W2370917603","https://openalex.org/W2389381914","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W2347897961"],"abstract_inverted_index":{"Change":[0],"detection":[1,11,101,136],"from":[2],"ground":[3],"vehicles":[4],"has":[5,57],"various":[6],"applications,":[7],"such":[8,90],"as":[9],"the":[10,43,71,85,100],"of":[12,34,37,73,89,102,141,144,158,162,186],"roadside":[13],"Improvised":[14],"Explosive":[15],"Devices":[16],"(IEDs).":[17],"Although":[18],"IEDs":[19],"are":[20,23],"hidden,":[21],"they":[22],"often":[24],"accompanied":[25],"by":[26,191],"visible":[27],"markers,":[28],"which":[29],"can":[30],"be":[31,53],"any":[32,39],"kind":[33],"object.":[35],"Because":[36],"this,":[38],"suspicious":[40],"change":[41,135],"in":[42,50,92,105,118,165,184],"environment":[44],"compared":[45,108],"to":[46,60,79,82,109,132],"an":[47,110,121],"earlier":[48,111],"moment":[49],"time,":[51],"should":[52],"detected.":[54],"Little":[55],"work":[56],"been":[58],"published":[59],"solve":[61],"this":[62],"ill-posed":[63],"problem":[64],"using":[65],"deep":[66],"learning.":[67],"This":[68],"paper":[69],"shows":[70],"feasibility":[72],"applying":[74],"convolutional":[75],"neural":[76],"networks":[77,178],"(CNNs)":[78],"HD":[80,106],"video,":[81,107],"accurately":[83],"predict":[84],"presence":[86],"and":[87,124,179],"location":[88],"markers":[91],"real":[93],"time.":[94],"The":[95,152,172],"network":[96,154,174],"is":[97,156],"trained":[98],"for":[99],"pixel-level":[103,134],"changes":[104],"reference":[112],"recording.":[113],"We":[114],"investigate":[115],"Siamese":[116],"CNNs":[117],"combination":[119],"with":[120,148],"encoder-decoder":[122],"architecture":[123,155],"introduce":[125],"a":[126,169],"modified":[127],"double-margin":[128],"contrastive":[129],"loss":[130],"function,":[131],"achieve":[133],"results.":[137],"Our":[138],"dataset":[139,183],"consists":[140],"seven":[142],"pairs":[143],"challenging":[145],"real-world":[146],"recordings":[147],"geo-tagged":[149],"test":[150],"objects.":[151],"proposed":[153,173],"capable":[157],"comparing":[159],"two":[160],"images":[161],"1920×1440":[163],"pixels":[164],"150":[166],"ms":[167],"on":[168,181,189],"GTX1080Ti":[170],"GPU.":[171],"significantly":[175],"outperforms":[176],"state-of-the-art":[177],"algorithms":[180],"our":[182],"terms":[185],"F-1":[187],"score,":[188],"average":[190],"0.28.":[192]},"counts_by_year":[{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}