{"id":"W3215468872","doi":"10.18280/ts.380525","title":"FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; False alarm; Computer science; Convolutional neural network; Filter (signal processing); Artificial intelligence; Constant false alarm rate; Generalization; ALARM; Pattern recognition (psychology); Real-time computing; Computer vision; Algorithm; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001771616,0.0002607499,0.0002217722,0.00008445384,0.0001218568,0.0000843815,0.00007518046,0.0001251588,0.0004137397],"category_scores_gemma":[0.000008249302,0.0002881062,0.0001551681,0.0002201232,0.0000136986,0.0001403705,0.00001244731,0.0002046769,0.00006255668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000421,"about_ca_system_score_gemma":0.00001260946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003161953,"about_ca_topic_score_gemma":0.0000374816,"domain_scores_codex":[0.9985923,0.00005640755,0.0003217859,0.0003089006,0.0002380025,0.000482641],"domain_scores_gemma":[0.9994008,0.000185883,0.00004801082,0.0001567979,0.00008732868,0.0001211627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008297188,0.00007889702,0.00002483868,0.00006055947,0.00008371052,0.00005375753,0.00007766057,0.4508888,0.01296409,0.000005307054,0.0003344762,0.535345],"study_design_scores_gemma":[0.001639979,0.000200027,0.0007991109,0.00004961632,0.00006628901,0.00002464875,0.00002250809,0.9629167,0.03084263,0.00006527799,0.003064293,0.0003089125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2541625,0.0001911582,0.7429551,0.0001268571,0.0007867415,0.0007256258,0.0001127676,0.0005626264,0.0003766204],"genre_scores_gemma":[0.9876612,0.00001526408,0.01042546,0.0005594208,0.0007944714,0.0002027593,0.0002363457,0.00008253237,0.00002250214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7334988,"threshold_uncertainty_score":0.9999571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01315371317435439,"score_gpt":0.2053627368788887,"score_spread":0.1922090237045344,"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."}}