{"id":"W4403677691","doi":"10.1109/issi63632.2024.10720508","title":"Lightweight Convolutional Neural Network-based Drone Detection Using Radar Spectrograms","year":2024,"lang":"en","type":"article","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; National Research Council Canada; Defence Research and Development Canada","funders":"","keywords":"Spectrogram; Drone; Convolutional neural network; Computer science; Radar; Artificial intelligence; Computer vision; Speech recognition; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000284879,0.0007182474,0.0003879522,0.0004839406,0.0001418614,0.0003463261,0.0006940157,0.0003457151,0.001500086],"category_scores_gemma":[0.0008075413,0.0002068445,0.00025095,0.0002847676,0.0001725623,0.0006215628,0.0005215612,0.0004172137,0.0006410741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507949,"about_ca_system_score_gemma":0.0004078454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007039049,"about_ca_topic_score_gemma":0.01123965,"domain_scores_codex":[0.9998446,0.00001338352,0.000007159115,0.00004711081,0.00004787077,0.00003984258],"domain_scores_gemma":[0.9998036,0.00005051607,0.00002916914,0.00003865788,0.00006509513,0.00001294219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004994981,0.0003255165,0.01164786,0.0002099877,0.0002132945,0.0003359683,0.00005967813,0.3550146,0.09475069,0.001730605,0.005250289,0.5299619],"study_design_scores_gemma":[0.000006462637,0.00007544784,0.003015544,0.00001340786,0.00002099864,0.00007581106,0.00001256746,0.9780126,0.01739357,0.0003357405,0.001028861,0.000008924062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4596121,0.001371799,0.52463,0.0002996838,0.0002495214,0.0001407659,0.001068239,0.005116274,0.00751167],"genre_scores_gemma":[0.9361284,0.0002846305,0.05737987,0.0001168581,0.00003676209,0.0000499396,0.001251193,0.00005539694,0.004696951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007039049,"threshold_uncertainty_score":0.01399612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008065428983899147,"score_gpt":0.2030884385238892,"score_spread":0.1950230095399901,"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."}}