{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001458062,0.0001151041,0.0001279744,0.0001331362,0.0001131853,0.00005529474,0.00003657992,0.0001190023,0.00002577559],"category_scores_gemma":[0.000007389422,0.0001364453,0.00002439159,0.0003005324,0.00002238291,0.0001563683,0.00001588459,0.0001362133,0.00001763789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001820925,"about_ca_system_score_gemma":0.00001449945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000688814,"about_ca_topic_score_gemma":0.00001579309,"domain_scores_codex":[0.9992332,0.00002912087,0.000226092,0.0001892062,0.0001273293,0.0001950344],"domain_scores_gemma":[0.999723,0.00002840551,0.00004437673,0.00008855607,0.00008034903,0.00003537937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005694344,0.00001309895,0.001004295,0.0001728472,0.0000403936,0.00001503264,0.00008409561,0.00233346,0.9507589,0.0001728028,0.000004655131,0.04539471],"study_design_scores_gemma":[0.0003894297,0.00002109056,0.004360453,0.00009763057,0.00004805352,0.0001687081,0.0008767346,0.7770893,0.2165119,0.00003615262,0.0002443176,0.0001562215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745579,0.0003053063,0.0238183,0.00004049492,0.0001463228,0.0001324788,0.000005723567,0.0005197159,0.0004738022],"genre_scores_gemma":[0.9989029,0.00002658071,0.000899766,0.00001051892,0.0001009218,0.00001893937,0.00001139436,0.0000255586,0.000003423307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7747558,"threshold_uncertainty_score":0.5564079,"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."}}