{"id":"W3215343428","doi":"10.1109/rose52750.2021.9611765","title":"Detection and Location of Sheet Metal Parts for Industrial Robots","year":2021,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Clutter; Artificial intelligence; Computer science; Computer vision; Robot; Set (abstract data type); Process (computing); Object (grammar); Pattern recognition (psychology); Sheet metal; Industrial robot; Object detection; Cognitive neuroscience of visual object recognition; Engineering; Radar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003234615,0.0007251557,0.0004595014,0.001363998,0.0002428288,0.0005320175,0.0007941452,0.00082631,0.001557025],"category_scores_gemma":[0.0007590772,0.0004204381,0.0005034049,0.000480351,0.000270548,0.0006272733,0.0005571173,0.000335074,0.001190322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002298935,"about_ca_system_score_gemma":0.0003197278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009760532,"about_ca_topic_score_gemma":0.001356857,"domain_scores_codex":[0.9994955,0.00004792133,0.00002222142,0.0001388512,0.0002534028,0.0000419636],"domain_scores_gemma":[0.9995796,0.0000938837,0.00008797734,0.00009582053,0.0001232259,0.0000194986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002474622,0.0000617642,0.003036135,0.0001676907,0.00004258685,0.0001942823,0.00008922727,0.009806486,0.4023994,0.0003226883,0.0008979755,0.5827343],"study_design_scores_gemma":[0.0000270143,0.0008250856,0.04469035,0.00005740719,0.00008850663,0.002797448,0.000177808,0.4456971,0.4943145,0.001181948,0.01005891,0.00008390244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1259785,0.0005649762,0.8690493,0.00005768888,0.00003680796,0.00006106226,0.00008988479,0.002936821,0.001224893],"genre_scores_gemma":[0.550054,0.0003452609,0.4457779,0.00006758033,0.0000212099,0.00005688637,0.0002631716,0.0001244047,0.003289743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001557025,"threshold_uncertainty_score":0.005208731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04041400744362248,"score_gpt":0.2400643760809298,"score_spread":0.1996503686373073,"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."}}