{"id":"W3203269111","doi":"10.18280/ts.380419","title":"Vehicle Classification and Counting System Using YOLO Object Detection Technology","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Intelligent transportation system; Computer science; Convolutional neural network; Artificial intelligence; Computer vision; Object detection; Image processing; Real-time computing; Object (grammar); Line (geometry); Engineering; Pattern recognition (psychology); Image (mathematics); Mathematics; Transport engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002621091,0.0005662401,0.0006301809,0.001407833,0.0004600298,0.0007895364,0.0006887311,0.0004411692,0.002132018],"category_scores_gemma":[0.0004600642,0.000260831,0.0004026236,0.0007510679,0.0001773527,0.0005607316,0.000590065,0.0003553737,0.001545187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004703516,"about_ca_system_score_gemma":0.0006191004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007208614,"about_ca_topic_score_gemma":0.006362177,"domain_scores_codex":[0.9996344,0.00001722596,0.00002246567,0.0001392617,0.0001099042,0.00007673816],"domain_scores_gemma":[0.9998016,0.00001780496,0.00002729882,0.00002026394,0.0001160169,0.00001715706],"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":[0.0005872528,0.0004415041,0.02190834,0.0002720656,0.0001124369,0.0004432186,0.0002443096,0.02385676,0.1217292,0.002193196,0.006606395,0.8216052],"study_design_scores_gemma":[0.00004186668,0.0004233069,0.02987013,0.00005861409,0.0001517176,0.0004351966,0.000179026,0.8950292,0.0644521,0.0006371015,0.008648329,0.0000734591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3918166,0.0007327216,0.5838952,0.0002337833,0.0003600608,0.000416452,0.0009205907,0.006863458,0.01476109],"genre_scores_gemma":[0.8335255,0.0004956373,0.1484638,0.0001706827,0.00009306188,0.0003485668,0.001788363,0.00006444173,0.01504996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007208614,"threshold_uncertainty_score":0.01433331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01724058722605882,"score_gpt":0.2063798426011502,"score_spread":0.1891392553750914,"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."}}