{"id":"W4392358221","doi":"10.18280/ria.380114","title":"Superior Use of YOLOv8 to Enhance Car License Plates Detection Speed and Accuracy","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; Computer science; Automotive engineering; Artificial intelligence; 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":[0.0006493253,0.0007049301,0.0005690983,0.001072904,0.0002278562,0.0006226469,0.0008608744,0.0003985465,0.002100554],"category_scores_gemma":[0.001918844,0.0001938402,0.0004118259,0.0004927191,0.0002377894,0.0007769169,0.0005410165,0.0004060384,0.001102791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006124676,"about_ca_system_score_gemma":0.0007641238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008792077,"about_ca_topic_score_gemma":0.01055186,"domain_scores_codex":[0.9996161,0.0000489341,0.00003195175,0.000119597,0.000118972,0.00006442137],"domain_scores_gemma":[0.9994739,0.0001316508,0.00005068932,0.0000664774,0.0002447958,0.00003256981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001528786,0.0002632932,0.0142244,0.0005058101,0.000166113,0.0002321244,0.0001058867,0.07163489,0.1105282,0.001325765,0.008131656,0.791353],"study_design_scores_gemma":[0.00007580232,0.0008690121,0.01753829,0.00006731385,0.0001258172,0.0004806233,0.000125107,0.8113672,0.1556613,0.0005839971,0.01302958,0.00007589498],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8003758,0.004334089,0.1678534,0.0003637353,0.0005594948,0.0002002683,0.001081368,0.0118792,0.01335264],"genre_scores_gemma":[0.8828085,0.0006703299,0.1044695,0.000172919,0.00005827794,0.00006374087,0.003046877,0.0002580514,0.008451868],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008792077,"threshold_uncertainty_score":0.0174818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171868288744403,"score_gpt":0.2680201942981091,"score_spread":0.236301511410665,"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."}}