论文标题
标签不是完美的：通过标签不确定性改善概率对象检测
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
论文作者
论文摘要
可靠的不确定性估计对于自动驾驶中的可靠对象检测至关重要。但是，以前的概率对象检测的作品要么以无监督的方式学习边界框回归的预测概率，要么使用简单的启发式方法来进行不确定性的正则化。这会导致不稳定的训练或次优的检测性能。在这项工作中，我们利用了先前提出的方法来估计地面真相边界框参数固有的不确定性（我们称标签不确定性）来提高基于概率激光雷达对象检测器的检测准确性。 KITTI数据集的实验结果表明，我们的方法在平均精度方面超过了基线模型和基于简单的启发式方法的模型高达3.6％。
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.
