{"id":"W4389264937","doi":"10.1016/j.eswa.2023.122749","title":"Computer vision defect detection on unseen backgrounds for manufacturing inspection","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Deep learning; Visual inspection; Task (project management); Machine learning; Variety (cybernetics); Object detection; Parameterized complexity; Pattern recognition (psychology); Computer vision; Engineering","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":[0.0002604883,0.0002257387,0.0002342711,0.0003484484,0.0004483189,0.0001435059,0.0000982772,0.0001863233,0.000003190299],"category_scores_gemma":[0.000003471391,0.0001917764,0.0001098075,0.000518533,0.0000177861,0.000152936,0.00001253494,0.0001530554,0.0003210647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002395592,"about_ca_system_score_gemma":0.00001015444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009662503,"about_ca_topic_score_gemma":0.00002791478,"domain_scores_codex":[0.9987409,0.00003881002,0.0003358381,0.0003581698,0.0002386983,0.0002876534],"domain_scores_gemma":[0.9992373,0.0001457864,0.00007718971,0.0003820867,0.00006793626,0.00008972659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004910002,0.000154499,0.00006588841,0.0007209014,0.0004750851,0.00000788314,0.00112809,0.6429979,0.05385203,0.002181915,0.05147882,0.246446],"study_design_scores_gemma":[0.001764224,0.001104149,0.001016621,0.0002894602,0.00003196838,0.00008850598,0.0006256427,0.3427418,0.05093791,0.00005189849,0.6005939,0.0007539114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07590283,0.00008847671,0.9138957,0.00003200574,0.00183547,0.002977596,0.00002371128,0.003634957,0.00160921],"genre_scores_gemma":[0.9934527,0.00001586573,0.0002305638,0.00001634439,0.001763346,0.004122677,0.00005152785,0.00008212948,0.0002647922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9175499,"threshold_uncertainty_score":0.7820413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01970219987755033,"score_gpt":0.2603581894886739,"score_spread":0.2406559896111236,"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."}}