{"id":"W4386528487","doi":"10.1080/2150704x.2023.2254912","title":"PRO-YOLOv4-tiny: towards more balance between accuracy and speed in the detection of small targets in remotely sensed images","year":2023,"lang":"en","type":"article","venue":"Remote Sensing Letters","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nanjing University of Aeronautics and Astronautics; Ministry of Education; Ministry of Natural Resources","keywords":"Computer science; Key (lock); Pyramid (geometry); Remote sensing; Pooling; Fuse (electrical); Artificial intelligence; Drone; Computer vision; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001096962,0.0002058753,0.0003314191,0.0004520951,0.00004900656,0.00003462268,0.0001216656,0.0001331265,7.350117e-7],"category_scores_gemma":[0.001330618,0.0001870484,0.00005134501,0.00108055,0.00009186989,0.0001077028,0.00004215046,0.0004707511,0.000006881205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009539361,"about_ca_system_score_gemma":0.00001288031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003206606,"about_ca_topic_score_gemma":0.0000301996,"domain_scores_codex":[0.9984316,0.000346045,0.0003818145,0.0002651996,0.0001911629,0.0003842234],"domain_scores_gemma":[0.9987878,0.0007679549,0.00008871989,0.0002888378,0.00003922618,0.00002745379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002097927,0.000001384725,0.0003973369,0.0001224502,0.00001574571,0.0001136676,0.001649911,0.01071219,0.8518038,1.784994e-7,0.00008660401,0.1350757],"study_design_scores_gemma":[0.0006124796,0.00002490345,0.2287698,0.0001649306,0.00001977652,0.00006988338,0.0007633449,0.1633799,0.6053343,0.0002319793,0.0002957157,0.0003329295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750341,0.00007778047,0.02227032,0.00157879,0.0002593843,0.0003296799,0.000005390741,0.0003292113,0.000115324],"genre_scores_gemma":[0.9537783,0.00007975115,0.04573715,0.0002200913,0.0001246018,1.580241e-7,0.000007019077,0.00004241083,0.00001047303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2464695,"threshold_uncertainty_score":0.7627611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0465147078647677,"score_gpt":0.2779590491685645,"score_spread":0.2314443413037968,"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."}}