{"id":"https://openalex.org/W7225661012","doi":"https://doi.org/10.3390/s26217092","title":"GRACE-YOLOv7: An Improved Deep Learning Method for Object Detection in Remote Sensing Images","display_name":"GRACE-YOLOv7: An Improved Deep Learning Method for Object Detection in Remote Sensing Images","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225661012","doi":"https://doi.org/10.3390/s26217092"},"language":"en","primary_location":{"id":"doi:10.3390/s26217092","doi":"https://doi.org/10.3390/s26217092","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s26217092","pdf_url":null,"source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","jufo-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/s26217092","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161305476","display_name":"Xiang Gao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Gao","raw_affiliation_strings":["College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074885009","display_name":"Yang Changbao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changbao Yang","raw_affiliation_strings":["College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086673970","display_name":"Liguo Han","orcid":"https://orcid.org/0000-0003-4840-8091","observed_orcids":["https://orcid.org/0000-0003-4840-8091"]},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liguo Han","raw_affiliation_strings":["College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China"],"raw_orcid":"https://orcid.org/0000-0003-4840-8091","affiliations":[{"raw_affiliation_string":"College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5074885009"],"corresponding_institution_ids":["https://openalex.org/I194450716"],"apc_list":{"value":2600,"currency":"CHF","value_usd":3137},"apc_paid":{"value":2600,"currency":"CHF","value_usd":3137},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":"21","first_page":"7092","last_page":"7092"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9951000213623047,"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":0.9951000213623047,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.0027000000700354576,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.0005000000237487257,"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/yolov7","display_name":"YOLOv7","score":0.875},{"id":"https://openalex.org/keywords/object-detection","display_name":"object detection","score":0.75},{"id":"https://openalex.org/keywords/remote-sensing-imagery","display_name":"remote sensing images","score":0.9950000047683716},{"id":"https://openalex.org/keywords/multi-scale-object-detection","display_name":"multi-scale object detection","score":0.9139999747276306},{"id":"https://openalex.org/keywords/deformable-convolution","display_name":"deformable convolution","score":0.7559999823570251},{"id":"https://openalex.org/keywords/bi-level-routing-attention","display_name":"bi-level routing attention","score":0.9490000009536743},{"id":"https://openalex.org/keywords/mpdiou-loss","display_name":"MPDIoU loss","score":0.7369999885559082},{"id":"https://openalex.org/keywords/small-object-detection","display_name":"small object detection","score":0.8289999961853027},{"id":"https://openalex.org/keywords/bounding-box-regression","display_name":"bounding box regression","score":0.7519999742507935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7552000284194946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7282000184059143},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7279999852180481},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6233999729156494},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5943999886512756},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5913000106811523},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5532000064849854},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5419999957084656},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5141000151634216},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4790000021457672},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.45179998874664307},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4465000033378601},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.37929999828338623},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.3720000088214874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.32170000672340393},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2964000105857849},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.2962999939918518},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C2781302577","wikidata":"https://www.wikidata.org/wiki/Q209","display_name":"Rectangle","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.257999986410141}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/s26217092","doi":"https://doi.org/10.3390/s26217092","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s26217092","pdf_url":null,"source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","jufo-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/s26217092","doi":"https://doi.org/10.3390/s26217092","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s26217092","pdf_url":null,"source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","jufo-1","medline","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4411690939","https://openalex.org/W4396680782","https://openalex.org/W4410082589","https://openalex.org/W7126423958","https://openalex.org/W7212428700","https://openalex.org/W7084094864","https://openalex.org/W4366091744","https://openalex.org/W7202271608","https://openalex.org/W7161123345","https://openalex.org/W7125239572"],"abstract_inverted_index":{"The":[0],"substantial":[1],"variation":[2],"in":[3,13,49,71,111],"object":[4,47,187,254],"scales,":[5],"complex":[6,126],"spatial":[7,78],"distributions,":[8],"and":[9,25,30,94,121,141,165,203,211,218,223,230,235,240,264],"rich":[10],"background":[11,127],"information":[12,116,128],"remote":[14,50],"sensing":[15,51],"images":[16,52],"can":[17],"lead":[18],"to":[19,88,114,154,159,173,184,245],"inadequate":[20],"feature":[21,69,108,131],"representation,":[22],"missed":[23],"detections,":[24],"inaccurate":[26],"localization":[27],"of":[28,125,163,206],"small":[29,164,263],"densely":[31,166,265],"distributed":[32,167,266],"objects.":[33,168,267],"To":[34],"address":[35],"these":[36],"issues,":[37],"this":[38,134],"study":[39],"proposes":[40],"GRACE-YOLOv7,":[41],"an":[42,57,142],"improved":[43],"network":[44],"for":[45,262],"multi-scale":[46,130,253],"detection":[48,144,152,157,255],"based":[53],"on":[54,191,257],"YOLOv7.":[55],"First,":[56],"ELAN-D":[58],"module":[59,83],"incorporating":[60],"deformable":[61],"convolution":[62],"v3":[63],"(DCNv3)":[64],"is":[65,104,146,171],"constructed":[66],"during":[67],"deep":[68],"extraction":[70],"the":[72,77,81,85,112,123,149,161,175,179,192,258],"backbone.":[73],"By":[74],"adaptively":[75],"adjusting":[76],"sampling":[79],"locations,":[80],"proposed":[82],"enhances":[84],"network\u2019s":[86],"ability":[87],"represent":[89],"objects":[90],"with":[91,129,215],"different":[92],"scales":[93],"geometric":[95],"structures.":[96],"Second,":[97],"a":[98,155],"Bi-level":[99],"Routing":[100],"Attention":[101],"(BRA)":[102],"mechanism":[103],"introduced":[105],"at":[106],"key":[107],"interaction":[109,117],"positions":[110],"neck":[113],"strengthen":[115],"among":[118],"target-relevant":[119],"regions":[120],"reduce":[122],"interference":[124],"fusion.":[132],"On":[133],"basis,":[135],"shallow":[136],"high-resolution":[137],"features":[138],"are":[139],"incorporated":[140],"additional":[143],"branch":[145],"introduced,":[147],"extending":[148],"original":[150,176],"three-scale":[151],"structure":[153,158],"four-scale":[156],"enhance":[160],"representation":[162],"Meanwhile,":[169],"MPDIoU":[170],"adopted":[172],"replace":[174],"CIoU":[177],"as":[178],"bounding":[180],"box":[181],"regression":[182],"loss":[183],"further":[185],"improve":[186],"localization.":[188],"Experimental":[189],"results":[190,248],"DOTA-v1.0":[193,259],"dataset":[194],"show":[195],"that":[196,250],"GRACE-YOLOv7":[197,251],"achieves":[198],"mAP0.5,":[199],"mAP0.75,":[200],"Recall,":[201],"Precision,":[202],"F1":[204,241],"scores":[205],"86.09%,":[207],"65.42%,":[208],"84.40%,":[209],"82.60%,":[210],"0.8347,":[212],"respectively.":[213],"Compared":[214],"YOLOv7,":[216],"mAP0.5":[217],"mAP0.75":[219],"increase":[220,232],"by":[221,233],"2.35":[222],"3.28":[224],"percentage":[225,237],"points,":[226,238],"respectively,":[227,239],"while":[228],"Recall":[229],"Precision":[231],"2.81":[234],"1.63":[236],"increases":[242],"from":[243],"0.8093":[244],"0.8347.":[246],"These":[247],"demonstrate":[249],"improves":[252],"performance":[256],"dataset,":[260],"particularly":[261]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
