{"id":"W6884637092","doi":"10.1109/tim.2025.3548193","title":"SFC-YOLOv8: Enhanced Strip Steel Surface Defect Detection Using Spatial-Frequency Domain-Optimized YOLOv8","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Science and Technology Program of Hubei Province; National Natural Science Foundation of China","keywords":"STRIPS; Feature extraction; Boosting (machine learning); Robustness (evolution); Noise (video); Frequency domain; Strip steel; Sensitivity (control systems); Feature (linguistics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003336036,0.0002640317,0.0002272397,0.0002203713,0.0006437998,0.0001266608,0.0002530598,0.00009701918,0.0000177244],"category_scores_gemma":[0.000007145906,0.0002777184,0.0001196528,0.0007412699,0.00007061387,0.0004896854,0.000005670635,0.00024634,0.00001279599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005546465,"about_ca_system_score_gemma":0.0001326309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001172955,"about_ca_topic_score_gemma":0.0002759565,"domain_scores_codex":[0.997941,0.0001770089,0.0004567256,0.0006093369,0.0005041205,0.0003118102],"domain_scores_gemma":[0.9989773,0.00007090172,0.0001746973,0.0004586485,0.0002044378,0.0001139984],"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":[0.0001317574,0.0003012202,0.00002522464,0.00004156012,0.0001615657,0.000001273023,0.00047853,0.09453669,0.6673825,0.001533331,0.00001161405,0.2353947],"study_design_scores_gemma":[0.004417428,0.0003069662,0.0007090113,0.0001566965,0.0001506845,0.00001345702,0.0003398666,0.07179908,0.9159992,0.005273974,0.0002701671,0.0005635111],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1431901,0.00005695198,0.8540766,0.0003361878,0.000861783,0.0007985157,0.000006216839,0.0002043834,0.000469251],"genre_scores_gemma":[0.9088401,0.0001004737,0.09054329,0.0003015288,0.00001724706,0.0001265554,0.000001856042,0.00001330375,0.00005561197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.76565,"threshold_uncertainty_score":0.9999675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03034928841534202,"score_gpt":0.2711121571593537,"score_spread":0.2407628687440116,"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."}}