Uncertainty Estimation in One-Stage Object Detection
This work addresses the need for reliable uncertainty estimation in object detection for intelligent vehicles, which is incremental as it builds on existing one-stage detectors.
The paper tackles the problem of uncertainty estimation in one-stage object detectors for autonomous driving, showing that the proposed approach improves baseline detection performance and outputs uncertainty correlated with detection accuracy and pedestrian occlusion levels.
Environment perception is the task for intelligent vehicles on which all subsequent steps rely. A key part of perception is to safely detect other road users such as vehicles, pedestrians, and cyclists. With modern deep learning techniques huge progress was made over the last years in this field. However such deep learning based object detection models cannot predict how certain they are in their predictions, potentially hampering the performance of later steps such as tracking or sensor fusion. We present a viable approaches to estimate uncertainty in an one-stage object detector, while improving the detection performance of the baseline approach. The proposed model is evaluated on a large scale automotive pedestrian dataset. Experimental results show that the uncertainty outputted by our system is coupled with detection accuracy and the occlusion level of pedestrians.