{"id":"W2903967792","doi":"10.1155/2018/2365414","title":"An Efficient Color Space for Deep-Learning Based Traffic Light Recognition","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Ministry of Education; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Artificial intelligence; Computer science; RGB color model; YCbCr; HSL and HSV; Deep learning; Color space; Computer vision; Traffic signal; Task (project management); Pattern recognition (psychology); Image processing; Real-time computing; Color image; 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":[0.0004421997,0.0001109334,0.0001560541,0.0001928044,0.0001281101,0.00005304064,0.0002632091,0.00004848151,0.00001000071],"category_scores_gemma":[0.00004101162,0.0001054775,0.00009002457,0.0002601277,0.00002653076,0.0008563721,0.000001276638,0.0001271521,0.000003258758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006134107,"about_ca_system_score_gemma":0.00005721221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.524923e-7,"about_ca_topic_score_gemma":0.00001252757,"domain_scores_codex":[0.9989058,0.00004585972,0.0004050878,0.0001867263,0.0002767968,0.0001796632],"domain_scores_gemma":[0.9985207,0.00006460819,0.0004881258,0.0001315857,0.0007202055,0.0000747821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000667086,0.0004931653,0.00005587847,0.00007328134,0.00002812193,0.00002613196,0.005373352,0.4058662,0.2701869,0.0005753717,0.00008387627,0.3165706],"study_design_scores_gemma":[0.002554553,0.005885979,0.005735132,0.0002212733,0.00006184546,0.00001006541,0.0003171144,0.291551,0.6889758,0.0007180817,0.003622946,0.0003462106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3591615,0.00002142101,0.6400623,0.0002207686,0.0002595408,0.0001793944,0.00000117844,0.00007867855,0.00001518573],"genre_scores_gemma":[0.613879,0.000005880619,0.3859227,0.00005733999,0.00009931094,0.00001145948,0.00001008343,0.000008808542,0.000005408716],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4187889,"threshold_uncertainty_score":0.430125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009293734751485604,"score_gpt":0.2674057458900855,"score_spread":0.2581120111385999,"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."}}