{"id":"W3160162937","doi":"10.18280/ts.380223","title":"Real-Time Obstacle Detection Based on Image Semantic Segmentation and Fusion Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Convolutional neural network; Obstacle; Segmentation; Image segmentation; RGB color model; Robot; Image (mathematics); Aerial image; Set (abstract data type); Pattern recognition (psychology); Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003508707,0.0009749022,0.0007348729,0.0008426773,0.000408569,0.0005289289,0.001108697,0.0008549783,0.00107729],"category_scores_gemma":[0.0006535844,0.0003727429,0.0006701512,0.0005504786,0.0005455699,0.001470111,0.0009861966,0.0005546736,0.0002724857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00080799,"about_ca_system_score_gemma":0.0007125404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006129894,"about_ca_topic_score_gemma":0.004930474,"domain_scores_codex":[0.9997076,0.00001823922,0.00001187785,0.0001176529,0.00008536863,0.00005918164],"domain_scores_gemma":[0.9998001,0.00003474918,0.00003368102,0.00002053131,0.00008809398,0.00002275417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006151624,0.0003020496,0.004437103,0.0001500631,0.0001496308,0.0003219881,0.0002173846,0.3325534,0.1001851,0.004732621,0.003768388,0.5525671],"study_design_scores_gemma":[0.000005221648,0.00005284396,0.0009402294,0.000004612401,0.00002447957,0.00004250353,0.000019156,0.9853817,0.01159377,0.001470702,0.0004528516,0.00001185884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1190905,0.0003993048,0.8741777,0.0001768398,0.00009487388,0.00007800898,0.0001502849,0.002699879,0.00313275],"genre_scores_gemma":[0.8736492,0.0002065106,0.122722,0.0001355848,0.00002926232,0.00007197483,0.0003434047,0.00006997402,0.002772051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006129894,"threshold_uncertainty_score":0.01218843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008428754405628269,"score_gpt":0.1940629262158043,"score_spread":0.185634171810176,"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."}}