{"id":"W4408520843","doi":"10.1109/tim.2025.3551917","title":"MIS-YOLOv8: An Improved Algorithm for Detecting Small Objects in UAV Aerial Photography Based on YOLOv8","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China; National Foundation for Science and Technology Development","keywords":"Aerial photography; Computer vision; Photography; Computer science; Artificial intelligence; Remote sensing; Computer graphics (images); Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003016886,0.0001909996,0.0001550649,0.0004012589,0.0003242112,0.00009514228,0.0002086774,0.00006961293,0.000003648856],"category_scores_gemma":[0.00000621572,0.0002023474,0.00007508433,0.0006215802,0.00003426245,0.0002202376,0.000001258327,0.0001655768,0.000001185567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001874902,"about_ca_system_score_gemma":0.00009404765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005214462,"about_ca_topic_score_gemma":0.000568308,"domain_scores_codex":[0.998583,0.00008079944,0.0003188153,0.0005239826,0.000230784,0.0002626399],"domain_scores_gemma":[0.9992988,0.00008641047,0.0000921064,0.0003227759,0.0001105999,0.00008935439],"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":[0.0001284569,0.0003826687,0.00001449005,0.00002209922,0.00002301938,4.804229e-7,0.0002885894,0.01008874,0.03573751,0.0001403627,0.000006980054,0.9531666],"study_design_scores_gemma":[0.003823773,0.0006523118,0.000297821,0.00008428928,0.00002758312,0.000001455951,0.0002511017,0.6724488,0.3214135,0.000491548,0.0002512165,0.0002566109],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01505291,0.000009694395,0.9823374,0.000328156,0.0007270234,0.001253733,0.00001203196,0.0001307119,0.0001483021],"genre_scores_gemma":[0.8780693,0.000008839091,0.1201031,0.0008776695,0.000025133,0.0008866671,0.000003814325,0.00001098941,0.00001446825],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.95291,"threshold_uncertainty_score":0.8251489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.032915944029324,"score_gpt":0.2721550418839406,"score_spread":0.2392390978546166,"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."}}