A review and comparative study on probabilistic object detection in autonomous driving
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …
recent years, deep learning has become the de-facto approach for object detection, and …
Uncertainty for identifying open-set errors in visual object detection
Deployed into an open world, object detectors are prone to open-set errors, false positive
detections of object classes not present in the training dataset. We propose GMM-Det, a real …
detections of object classes not present in the training dataset. We propose GMM-Det, a real …
Small aircraft detection using deep learning
Purpose The paper aims to address the tracking algorithm based on deep learning and four
deep learning tracking models developed. They compared with each other to prevent …
deep learning tracking models developed. They compared with each other to prevent …
Estimating and evaluating predictive uncertainty in deep object detectors
A Harakeh - 2021 - search.proquest.com
Object detection is a robot perception task that requires classifying objects in the scene into
one of many predefined categories, as well as localizing these objects through estimating …
one of many predefined categories, as well as localizing these objects through estimating …
[PDF][PDF] Efficient uncertainty estimation for open-set object detection
W Stevens - … Uncertainty Estimation for Object Detection in Open …, 2021 - eprints.qut.edu.au
Deployed into an open world, object detectors are prone to open-set errors, false positive
detections of object classes not present in the training dataset. We propose GMM-Det, a real …
detections of object classes not present in the training dataset. We propose GMM-Det, a real …