{"id":"W4390148988","doi":"10.23977/acss.2023.071015","title":"Improved Lightweight Rebar Detection Network Based on YOLOv8s Algorithm","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Rebar; Upsampling; Computer science; Robustness (evolution); Convolution (computer science); Pyramid (geometry); FLOPS; Artificial intelligence; Algorithm; Artificial neural network; Engineering; Structural engineering; Parallel computing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002222262,0.0001655221,0.0002259096,0.000120473,0.00007016921,0.00005946427,0.00008566022,0.00007882933,0.000001358528],"category_scores_gemma":[0.000002870725,0.0001417395,0.00003311818,0.0003065027,0.00001418209,0.0001548736,0.00002026662,0.0001503271,0.000007168178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004149947,"about_ca_system_score_gemma":0.000004463841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008240656,"about_ca_topic_score_gemma":0.000006514605,"domain_scores_codex":[0.999053,0.00003467101,0.0002493636,0.0002189207,0.0001109606,0.000333089],"domain_scores_gemma":[0.9996365,0.0001034449,0.00003673983,0.0001512591,0.00002431639,0.00004772052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005464003,0.000002435978,0.0001883832,0.00008625704,0.00000745488,0.00001713578,0.00005279223,0.8022312,0.0004759919,0.00003194715,0.0001709971,0.19673],"study_design_scores_gemma":[0.0002618981,0.0000968203,0.0003808445,0.000259114,0.000002715455,0.000004698078,0.00001523333,0.9858736,0.001025703,0.000194181,0.01171204,0.0001730887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01770313,0.003066912,0.9689891,0.00001272876,0.009031211,0.0003091541,0.000005086975,0.0004703435,0.000412356],"genre_scores_gemma":[0.9931084,0.0004302562,0.004260316,0.00003849256,0.002049889,0.00005361569,0.00000521963,0.00003006661,0.00002377569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9754052,"threshold_uncertainty_score":0.5779971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005425463834474335,"score_gpt":0.2100802509301845,"score_spread":0.2046547870957101,"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."}}