{"id":"W4401626964","doi":"10.3390/machines12080557","title":"KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions","year":2024,"lang":"en","type":"article","venue":"Machines","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Visibility; Artificial intelligence; Robustness (evolution); Computer vision; Object detection; Block (permutation group theory); Cluster analysis; Benchmark (surveying); Algorithm; Channel (broadcasting); Pattern recognition (psychology); Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005716161,0.0007924152,0.0008929149,0.001602106,0.0005244503,0.0009584433,0.001461235,0.0008577335,0.002120554],"category_scores_gemma":[0.00196742,0.0003764754,0.000612444,0.0007252348,0.0004593817,0.001095665,0.000993723,0.0008209532,0.001237752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007358929,"about_ca_system_score_gemma":0.001917662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01059812,"about_ca_topic_score_gemma":0.01903855,"domain_scores_codex":[0.9995221,0.00003787478,0.00002934654,0.0001544391,0.0001659558,0.00009028309],"domain_scores_gemma":[0.9993819,0.0001082213,0.0000788161,0.0000725234,0.000314757,0.00004367041],"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":[0.0005866739,0.0001794959,0.00530677,0.0002125667,0.00009143881,0.0001256571,0.0001818183,0.04879516,0.07780856,0.003023092,0.007970908,0.8557178],"study_design_scores_gemma":[0.00006205065,0.000207315,0.003541621,0.00003980759,0.00004714213,0.0001761694,0.0001032927,0.9509052,0.03586726,0.0009596481,0.008048556,0.00004186866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06010672,0.0007451391,0.9297906,0.0001633611,0.0001830804,0.0001425814,0.0001435458,0.005490687,0.003234243],"genre_scores_gemma":[0.3463634,0.0004765345,0.6433598,0.0003916996,0.00008471979,0.0002129967,0.00109791,0.000462625,0.007550337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01059812,"threshold_uncertainty_score":0.02107286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155313578785686,"score_gpt":0.2982213087616732,"score_spread":0.2866681729738163,"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."}}