{"id":"https://openalex.org/W4404293737","doi":"https://doi.org/10.1109/jiot.2024.3496748","title":"An Obstacle Recognition Model Based on Siamese Network With Masked Strategy for Unmanned Aerial Vehicle Obstacle Avoidance","display_name":"An Obstacle Recognition Model Based on Siamese Network With Masked Strategy for Unmanned Aerial Vehicle Obstacle Avoidance","publication_year":2024,"publication_date":"2024-11-12","ids":{"openalex":"https://openalex.org/W4404293737","doi":"https://doi.org/10.1109/jiot.2024.3496748"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2024.3496748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3496748","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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":null,"display_name":"Yang Lu","orcid":"https://orcid.org/0009-0007-2169-2818"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yang Lu","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K","Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom"],"raw_orcid":"https://orcid.org/0009-0007-2169-2818","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":null,"apc_paid":null,"fwci":1.1646,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.87665071,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"12","issue":"6","first_page":"6584","last_page":"6594"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9930999875068665,"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":0.9812999963760376,"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/obstacle","display_name":"Obstacle","score":0.8388542532920837},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.8084207773208618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7332373857498169},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.5133439898490906},{"id":"https://openalex.org/keywords/remotely-operated-underwater-vehicle","display_name":"Remotely operated underwater vehicle","score":0.4910731315612793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4875442683696747},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.326526403427124},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.316885769367218},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.19410768151283264},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.10664966702461243}],"concepts":[{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.8388542532920837},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.8084207773208618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7332373857498169},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.5133439898490906},{"id":"https://openalex.org/C145424490","wikidata":"https://www.wikidata.org/wiki/Q618465","display_name":"Remotely operated underwater vehicle","level":4,"score":0.4910731315612793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4875442683696747},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.326526403427124},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.316885769367218},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.19410768151283264},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.10664966702461243},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2024.3496748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3496748","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1493644597","https://openalex.org/W1526941208","https://openalex.org/W1674276221","https://openalex.org/W2044328399","https://openalex.org/W2051399256","https://openalex.org/W2091643732","https://openalex.org/W2169677720","https://openalex.org/W2238589392","https://openalex.org/W2530592706","https://openalex.org/W2586623916","https://openalex.org/W2597525528","https://openalex.org/W2909643277","https://openalex.org/W2922306179","https://openalex.org/W2945408524","https://openalex.org/W2964744899","https://openalex.org/W2979831320","https://openalex.org/W2985991446","https://openalex.org/W2991625383","https://openalex.org/W3005863531","https://openalex.org/W3017661315","https://openalex.org/W3042868593","https://openalex.org/W3125430184","https://openalex.org/W4205255149","https://openalex.org/W4221106701","https://openalex.org/W4224854590","https://openalex.org/W4226250616","https://openalex.org/W4310011176","https://openalex.org/W4311137818","https://openalex.org/W4316876987","https://openalex.org/W4319721356","https://openalex.org/W4323644096","https://openalex.org/W4365801681","https://openalex.org/W4386269701","https://openalex.org/W4387917979","https://openalex.org/W6632846619","https://openalex.org/W6803933528"],"related_works":["https://openalex.org/W2930076404","https://openalex.org/W4253519380","https://openalex.org/W2071957557","https://openalex.org/W2596413128","https://openalex.org/W4391249562","https://openalex.org/W2356867392","https://openalex.org/W2913749762","https://openalex.org/W2321940404","https://openalex.org/W2782776446","https://openalex.org/W3043170174"],"abstract_inverted_index":{"By":[0,211,251],"equipping":[1],"autonomous":[2,22],"aerial":[3],"vehicles":[4],"(AAVs)":[5],"with":[6,64,149,248,262],"multiple":[7],"sensors":[8],"to":[9,34,76,128,171,186,204,241],"gather":[10],"information":[11],"about":[12],"obstacles":[13,43,102],"in":[14,85,192,230,274],"their":[15],"flight":[16,25],"environment,":[17],"we":[18,139],"can":[19,62,117,221],"guide":[20],"the":[21,40,47,65,70,119,123,161,176,188,193,197,207,215,219,234,243,255,259,263,267,275,284,292],"and":[23,38,83,166,175,307],"safe":[24],"of":[26,42,58,69,97,101,114,122,163,190,209,214,246,286,302,305,309],"AAVs.":[27],"Existing":[28],"obstacle":[29,71,81,115,124,142,155,180],"avoidance":[30],"models":[31],"use":[32],"cameras":[33],"capture":[35],"environmental":[36,48],"images":[37,49],"identify":[39],"categories":[41,82,100,116,181,247],"within":[44],"them.":[45,270],"However,":[46],"captured":[50],"by":[51],"AAVs":[52,92],"often":[53],"contain":[54],"a":[55,94,201,238,300],"significant":[56],"amount":[57],"noise.":[59],"This":[60,111],"noise":[61,174,191],"interfere":[63],"feature":[66,194],"extraction":[67,195],"process":[68,121,245],"recognition":[72,125,143],"model,":[73,126],"causing":[74],"it":[75,170],"incorrectly":[77],"differentiate":[78],"between":[79,269],"various":[80],"resulting":[84],"decreased":[86],"classification":[87,130],"performance.":[88],"Additionally,":[89,233],"during":[90,217],"navigation,":[91],"encounter":[93],"wide":[95],"variety":[96],"obstacles.":[98],"Some":[99],"are":[103,108],"more":[104,223],"common,":[105],"while":[106],"others":[107],"less":[109],"frequent.":[110],"imbalanced":[112],"distribution":[113],"affect":[118],"training":[120,244],"leading":[127],"lower":[129],"accuracy":[131],"for":[132,153],"certain":[133,179],"categories.":[134],"To":[135],"address":[136],"these":[137],"challenges,":[138],"propose":[140],"an":[141],"model":[144,159,199,220,236,257,277,287,298],"based":[145],"on":[146,311],"siamese":[147,272],"network":[148],"masked":[150,164,202,212],"strategy":[151,203],"(ORSNMS)":[152],"AAV":[154],"avoidance.":[156],"The":[157,271,296],"ORSNMS":[158,198,235,256,276,297],"integrates":[160],"advantages":[162],"autoencoders":[165],"DenseNet":[167,239],"networks,":[168],"enabling":[169],"better":[172],"handle":[173],"situation":[177],"where":[178],"have":[182],"fewer":[183,249],"instances.":[184],"Specifically,":[185],"reduce":[187],"interference":[189],"process,":[196],"employs":[200],"further":[205],"learn":[206,222],"features":[208,261],"images.":[210],"part":[213],"data":[216],"training,":[218],"robust":[224],"image":[225],"features,":[226,265],"improving":[227],"its":[228],"performance":[229],"noisy":[231],"environments.":[232],"incorporates":[237],"structure":[240],"enhance":[242],"samples.":[250],"utilizing":[252],"contrastive":[253],"loss,":[254],"compares":[258],"enhanced":[260],"original":[264],"minimizing":[266],"error":[268],"subnetworks":[273],"share":[278],"parameters,":[279],"which":[280],"not":[281],"only":[282],"reduces":[283],"number":[285],"parameters":[288],"but":[289],"also":[290],"enhances":[291],"model\u2019s":[293],"generalization":[294],"capability.":[295],"achieves":[299],"Precision":[301],"0.984,":[303,306],"Recall":[304],"Accuracy":[308],"0.984":[310],"real":[312],"dataset.":[313]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
