{"id":"W3214592045","doi":"10.18280/ria.350510","title":"Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Field-programmable gate array; Computer science; Object detection; Deep learning; Process (computing); Artificial intelligence; Implementation; Artificial neural network; Function (biology); Object (grammar); Real-time computing; Resource (disambiguation); Task (project management); Embedded system; Computer vision; Computer engineering; Pattern recognition (psychology); Systems engineering; Engineering; Software engineering; Operating system","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":[0.0002881548,0.0001893816,0.0001716732,0.0001045886,0.0005018908,0.0002003832,0.0005526202,0.00007137654,0.0001820098],"category_scores_gemma":[0.00008446284,0.0002157246,0.0001000615,0.001173457,0.00005098905,0.000552384,0.0001230304,0.0002967572,0.0003488075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031478,"about_ca_system_score_gemma":0.00007538222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001827632,"about_ca_topic_score_gemma":0.0003170086,"domain_scores_codex":[0.997978,0.0002097579,0.0004267934,0.0007479987,0.0002275334,0.0004099651],"domain_scores_gemma":[0.9983575,0.0002085254,0.0001743305,0.0008857296,0.0002294334,0.0001445314],"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":[0.000003649165,0.00007167504,0.00006041491,0.000008898255,0.000004289513,0.00001734041,0.0003562291,0.552058,0.02167071,0.001158982,0.00001343201,0.4245764],"study_design_scores_gemma":[0.0000365204,0.0001045523,0.0001324375,0.000008225397,0.000005681218,0.00001767003,0.0003078547,0.616796,0.3765107,0.001469734,0.004443297,0.0001673917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01455098,0.0002288387,0.9834092,0.0005360356,0.0002764426,0.0002726416,0.00000122071,0.000372738,0.0003519302],"genre_scores_gemma":[0.9739903,0.00004971367,0.02504566,0.0002737677,0.0001143334,0.0001057579,0.00003367148,0.00002369408,0.0003631146],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9594393,"threshold_uncertainty_score":0.8796992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03143180504127736,"score_gpt":0.3055533501581832,"score_spread":0.2741215451169059,"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."}}