Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
This work addresses the need for more robust object detection in autonomous driving, though it is incremental as it builds on prior methods for label uncertainty.
The paper tackles the problem of unreliable uncertainty estimation in probabilistic object detection for autonomous driving by incorporating label uncertainty into a LiDAR-based detector, resulting in a 3.6% improvement in Average Precision on the KITTI dataset.
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive probability for bounding box regression in an un-supervised manner, or use simple heuristics to do uncertainty regularization. This leads to unstable training or suboptimal detection performance. In this work, we leverage our previously proposed method for estimating uncertainty inherent in ground truth bounding box parameters (which we call label uncertainty) to improve the detection accuracy of a probabilistic LiDAR-based object detector. Experimental results on the KITTI dataset show that our method surpasses both the baseline model and the models based on simple heuristics by up to 3.6% in terms of Average Precision.