{"id":"https://openalex.org/W4206807777","doi":"https://doi.org/10.1109/bigdata52589.2021.9671462","title":"Mask R-CNN: Detection Performance on SPEED Spacecraft With Image Degradation","display_name":"Mask R-CNN: Detection Performance on SPEED Spacecraft With Image Degradation","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206807777","doi":"https://doi.org/10.1109/bigdata52589.2021.9671462"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671462","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036328400","display_name":"Greg M. Murray","orcid":"https://orcid.org/0000-0003-1372-3489"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Greg Murray","raw_affiliation_strings":["Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056462004","display_name":"Thirimachos Bourlai","orcid":"https://orcid.org/0000-0001-8751-0836"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thirimachos Bourlai","raw_affiliation_strings":["School of Electrical & Computer Engineering, University of Georgia, Athens, Georgia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical & Computer Engineering, University of Georgia, Athens, Georgia","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064804156","display_name":"Max Spolaor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Max Spolaor","raw_affiliation_strings":["NASA Katherine Johnson IV&V, Independent Test Capability (ITC) TMC2 Technologies of WV Corp, Fairmont, West Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NASA Katherine Johnson IV&V, Independent Test Capability (ITC) TMC2 Technologies of WV Corp, Fairmont, West Virginia","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2864,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63439796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4183","last_page":"4190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983000159263611,"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":0.9983000159263611,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11701","display_name":"Space Satellite Systems and Control","score":0.991599977016449,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7748937606811523},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7675119042396545},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7542046308517456},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7394527196884155},{"id":"https://openalex.org/keywords/spacecraft","display_name":"Spacecraft","score":0.6944097280502319},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5637743473052979},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5626174807548523},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4874742031097412},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.4754702150821686},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4375610947608948},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.43520963191986084},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42113980650901794},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4072926640510559},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34284159541130066},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11232021450996399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7748937606811523},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7675119042396545},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7542046308517456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7394527196884155},{"id":"https://openalex.org/C29829512","wikidata":"https://www.wikidata.org/wiki/Q40218","display_name":"Spacecraft","level":2,"score":0.6944097280502319},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5637743473052979},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5626174807548523},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4874742031097412},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.4754702150821686},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4375610947608948},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.43520963191986084},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42113980650901794},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4072926640510559},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34284159541130066},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11232021450996399},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671462","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671462","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2344859357","https://openalex.org/W2901388191","https://openalex.org/W2905631704","https://openalex.org/W2908155087","https://openalex.org/W2933603317","https://openalex.org/W2944913849","https://openalex.org/W2948666538","https://openalex.org/W2963150697","https://openalex.org/W2963686971","https://openalex.org/W2972813610","https://openalex.org/W2985321619","https://openalex.org/W2996620874","https://openalex.org/W3001340251","https://openalex.org/W3005872834","https://openalex.org/W4289294527","https://openalex.org/W6620707391","https://openalex.org/W6755486015","https://openalex.org/W6762298766","https://openalex.org/W6770420337"],"related_works":["https://openalex.org/W4293211451","https://openalex.org/W3102253946","https://openalex.org/W4226289457","https://openalex.org/W4308191152","https://openalex.org/W3144574764","https://openalex.org/W2004370856","https://openalex.org/W4311401716","https://openalex.org/W2772397313","https://openalex.org/W2739874619","https://openalex.org/W1721780360"],"abstract_inverted_index":{"Convolutional":[0,56],"neural":[1],"networks":[2],"in":[3,14,67,77,91,103,110,175,259],"the":[4,15,53,63,98,104,128,152,176,217,224,229,233,268],"task":[5,197],"of":[6,62,69,100,136,154,169,178,188,204],"object":[7,71,94,192],"detection":[8,72,95,99,170,193,218],"and":[9,35,122,141,163,171,181,240],"localization":[10,172],"have":[11,23],"been":[12,24,48,60,201],"evolving":[13],"last":[16],"few":[17],"years.":[18],"Various":[19],"convolutional":[20],"network":[21],"models":[22,40,66,235],"proposed":[25,117,234,251],"such":[26],"as":[27],"Faster":[28],"Region-Based,":[29,31],"Mask":[30,54],"Single":[32],"Shot":[33],"Detection,":[34],"\"You":[36],"Only":[37],"Look":[38],"Once\"":[39],"(with":[41],"different":[42,79,137],"versions).":[43],"Although":[44],"instance":[45],"segmentation":[46],"has":[47,59,199],"explored":[49],"with":[50],"many":[51,78],"models,":[52],"Region-Based":[55],"Neural":[57],"Network":[58],"one":[61],"most":[64],"competitive":[65],"terms":[68],"overall":[70],"performance.":[73],"Its":[74],"widespread":[75],"use":[76],"applications":[80],"encouraged":[81],"us":[82],"to":[83,173,238,254,257,263],"take":[84],"a":[85,92,124,134],"closer":[86],"look":[87],"at":[88,142],"model":[89,252],"performance":[90,194],"unique":[93],"task,":[96],"namely":[97],"spacecraft":[101,125,244],"images":[102,214,245],"wild.":[105],"The":[106,146,167,186,250],"main":[107,206],"research":[108],"question":[109],"this":[111,115,149],"paper,":[112],"is":[113,151,195,226,236],"whether":[114],"off-the-shelf":[116],"architecture":[118],"can":[119],"effectively":[120],"detect":[121,239],"localize":[123,241],"when":[126],"using":[127],"Spacecraft":[129],"Pose":[130],"Estimation":[131],"Dataset,":[132],"under":[133],"variety":[135],"image":[138,161],"degradation":[139,144,222],"factors":[140],"various":[143],"levels.":[145],"inspiration":[147],"for":[148,267],"investigation":[150],"effect":[153],"deep":[155],"space":[156],"environments":[157],"on":[158,191,212,243],"charge-coupled":[159],"device":[160],"sensors,":[162],"other":[164],"imaging":[165],"hardware.":[166],"capability":[168],"continue":[174],"face":[177],"pixel":[179,248,265],"loss":[180,266],"Gaussian":[182],"noise":[183],"are":[184],"explored.":[185],"effects":[187],"training":[189,211],"augmentation":[190],"another":[196],"that":[198,209],"also":[200],"studied.":[202],"Some":[203],"our":[205],"findings":[207],"include":[208],"supplementing":[210],"degraded":[213,246],"improve":[215],"significantly":[216],"results.":[219,231],"In":[220],"low":[221],"scenarios,":[223],"improvement":[225],"better":[227],"than":[228],"baseline":[230,258],"Also,":[232],"able":[237],"properly":[242],"by":[247],"loss.":[249],"continues":[253],"perform":[255],"close":[256],"conditions":[260],"even":[261],"up":[262],"80%":[264],"black":[269],"background":[270],"experiments.":[271]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
