{"id":"W4310681647","doi":"10.20944/preprints202212.0049.v1","title":"Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Multirotor; Computer science; Artificial intelligence; Deep learning; Object detection; Real-time computing; Precision and recall; Single shot; Machine learning; Computer vision; Pattern recognition (psychology); Engineering; Aerospace engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001920415,0.0003324386,0.0004223592,0.0003786587,0.0003431583,0.0001140822,0.0008380316,0.0002201613,0.0002112707],"category_scores_gemma":[0.0003963484,0.000388763,0.0001278072,0.000339225,0.00006110295,0.0003125295,0.003255229,0.001789592,0.00009089673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001906954,"about_ca_system_score_gemma":0.00006144436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002072823,"about_ca_topic_score_gemma":0.000152279,"domain_scores_codex":[0.9966263,0.0008887935,0.0004770295,0.001257767,0.000315228,0.0004349464],"domain_scores_gemma":[0.9981821,0.000330521,0.0003151098,0.0009908126,0.00007544558,0.0001059784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003416585,0.0002080452,0.7345785,0.0002338915,0.000171702,0.0001320013,0.01365134,0.175965,0.01126286,0.001094911,8.455472e-7,0.0626667],"study_design_scores_gemma":[0.0005082211,0.00002422521,0.7845877,0.0001051254,0.00001243597,0.00002372944,0.0004817713,0.2072008,0.003072412,0.002647092,0.0008322429,0.0005043154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7810549,0.0002657951,0.214387,0.0003201538,0.0009403026,0.0004491073,0.000003620401,0.0004110018,0.00216813],"genre_scores_gemma":[0.9962167,0.0003121234,0.002827245,0.00004661111,0.00006452906,0.00025651,0.00001245055,0.00003664204,0.0002271611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2151618,"threshold_uncertainty_score":0.9998564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018974576007576,"score_gpt":0.3708826480551348,"score_spread":0.2689851904543772,"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."}}