… and do not take into account the uncertainty in predictions on out-of-… to model
uncertainty in object detection and segmentation tasks using Monte-Carlo DropBlock (MC-DropBlock) …

… uncertainty in the YOLOv5 object detection model, thereby improving the accuracy and speed
of probabilistic object detection, and … object detection model that offers enhanced detection …

… detections, it is referred to as Monte Carlo dropout [8]. … in object detection, DropBlock has
been proposed as an alternative method for modeling uncertainty estimation in object detection …

… complex tasks such as object detection and tracking or vehicle … that aims to model complex
aleatoric uncertainty (ambiguity, … [25] on 32 Monte Carlo samples (32 image forward passes …

… , we observe that an object detection model trained to recognize ID objects (eg, cars, …
minimizes its training error without explicitly accounting for the uncertainty that could appear …

… (1) object detection:We note that uncertainty research in the field of perception is mainly …
CK Mohan, “Monte carlo dropblock for modelling uncertainty in object detection,” arXiv, 2021. …

… object detection model … uncertainty modeling by dropping neurons in contiguous regions
of the feature map [36,37]. Hence, we propose Monte-Carlo (MC) DropBlock as an uncertainty …

… uncertainty estimation. Furthermore, the epistemic uncertainty obtained through Monte
Carlo … , highlighting its usefulness for detecting anomalous cases where the model makes …

… object detection model … uncertainty modeling by dropping neurons in contiguous regions
of the feature map [36,37]. Hence, we propose Monte-Carlo (MC) DropBlock as an uncertainty …

… a model’s predictive uncertainty … uncertainty for multi-object detection. In particular, we
leverage conformal prediction to obtain uncertainty intervals with guaranteed coverage for object …