{"id":"W4298009709","doi":"10.18280/ts.390407","title":"A Novel Detection Method Using YOLOv5 for Vehicle Target under Complex Situation","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Preprocessor; Histogram equalization; Brightness; Visibility; Convolution (computer science); Adaptive histogram equalization; Set (abstract data type); Image (mathematics); Object detection; Pattern recognition (psychology); Histogram; Data set; Contrast (vision); Artificial neural network","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":[0.0004411007,0.0005858936,0.000654372,0.001038953,0.0004063745,0.0007085658,0.001127752,0.0006356391,0.001910976],"category_scores_gemma":[0.0006947321,0.0003302484,0.0005592236,0.0005951066,0.0003464476,0.001097737,0.0007627117,0.0004637643,0.0008086494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005256485,"about_ca_system_score_gemma":0.0008258987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003766777,"about_ca_topic_score_gemma":0.004094257,"domain_scores_codex":[0.9995368,0.00004501073,0.00002469423,0.0001460332,0.0001730607,0.00007456286],"domain_scores_gemma":[0.9997246,0.00004668187,0.00003322116,0.00003621877,0.0001364904,0.0000227536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004840351,0.0001212587,0.005501738,0.0002680872,0.0001020588,0.0001781022,0.000198439,0.02770833,0.152846,0.005523392,0.002804488,0.8042642],"study_design_scores_gemma":[0.00005708888,0.0003705564,0.006292331,0.00002307882,0.00008441303,0.0006593156,0.00009506183,0.8713747,0.1101506,0.00107597,0.00974658,0.00007033366],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06556483,0.0004125849,0.9298505,0.00007475249,0.00009370681,0.00008256673,0.0000722456,0.001469165,0.002379673],"genre_scores_gemma":[0.4117562,0.0004432648,0.5815901,0.0001131542,0.00005008697,0.00008880444,0.0004122473,0.0001030266,0.005443198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003766777,"threshold_uncertainty_score":0.007489681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08399561364918205,"score_gpt":0.2958865058243789,"score_spread":0.2118908921751969,"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."}}