{"id":"W4388692010","doi":"10.1109/jsen.2023.3330146","title":"A Novel Keypoint Supplemented R-CNN for UAV Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Student Aid","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Object detection; Pyramid (geometry); Feature (linguistics); Pattern recognition (psychology); Backbone network; Computer vision; Set (abstract data type); Object (grammar); Deep learning; Aerial image; Data set; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003362096,0.0001379664,0.0001386094,0.0002163318,0.0004849561,0.0001481582,0.0004480671,0.00004656308,0.000008408323],"category_scores_gemma":[0.00006202857,0.0001289566,0.0001335412,0.0008769142,0.00002594489,0.0003365185,0.0000585453,0.0002534917,0.0001090622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009109419,"about_ca_system_score_gemma":0.00004110375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003552997,"about_ca_topic_score_gemma":0.00001643015,"domain_scores_codex":[0.9986359,0.00003519914,0.0003237009,0.000297122,0.0002620887,0.000446028],"domain_scores_gemma":[0.9989687,0.0002168687,0.0001831096,0.0003218958,0.0001643128,0.0001451109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000727233,0.0001277776,0.00005209011,0.00002000755,0.00009412569,0.00004951941,0.000723336,0.0932506,0.7638664,0.003047919,0.03434302,0.1043525],"study_design_scores_gemma":[0.001973481,0.0003212759,0.001107673,0.00003373846,0.00002369983,0.001482069,0.00010123,0.7176889,0.1960663,0.01404654,0.06668424,0.0004709146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04334376,0.00001003693,0.9527143,0.002116778,0.0010911,0.0003224325,0.00001125505,0.0003104061,0.00007988283],"genre_scores_gemma":[0.8745085,0.00007033411,0.1226166,0.0003990311,0.00116532,0.00009675972,0.000006049314,0.0000421271,0.001095314],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8311647,"threshold_uncertainty_score":0.5258698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03743363315938684,"score_gpt":0.3014553542170393,"score_spread":0.2640217210576525,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}