{"id":"W4400088899","doi":"10.23977/acss.2024.080405","title":"Optimization and performance evaluation of ship image recognition algorithm at night and in a fog","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Image fusion; Computer vision; Image processing; Optimization algorithm; Image (mathematics); Algorithm; Mathematics","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":[0.0009553336,0.0006911139,0.0006346974,0.000465386,0.0003235176,0.0006264157,0.0003952674,0.0006461355,0.0008284557],"category_scores_gemma":[0.002202119,0.0001532029,0.0003886416,0.0004304391,0.0002846582,0.0004135299,0.0002309526,0.0003025924,0.0002411866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006828227,"about_ca_system_score_gemma":0.0007214916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00798097,"about_ca_topic_score_gemma":0.003926751,"domain_scores_codex":[0.9995568,0.0001197182,0.00003400736,0.00008648486,0.0001308181,0.00007215483],"domain_scores_gemma":[0.9992006,0.0003756956,0.00006766108,0.00005063199,0.0002826537,0.000022697],"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":[0.000523233,0.0002187943,0.003061856,0.0001566641,0.00009627513,0.00009918839,0.0000635395,0.8707479,0.02570573,0.0008327506,0.0009609429,0.09753305],"study_design_scores_gemma":[0.00000851071,0.0001142886,0.001184473,0.000001984782,0.00001086661,0.00001761603,0.0000165672,0.9889904,0.009408121,0.00007517465,0.0001654424,0.000006665127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7116523,0.001051472,0.2783487,0.0002842899,0.00006519449,0.0001391958,0.0001117527,0.00110601,0.007241263],"genre_scores_gemma":[0.925839,0.0001930315,0.07189066,0.00004870934,0.000008703835,0.00007645681,0.0001342043,0.00007124254,0.00173784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00798097,"threshold_uncertainty_score":0.01586902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04188698214057022,"score_gpt":0.2885626327067009,"score_spread":0.2466756505661307,"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."}}