Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians and vehicles. One of the major challenges in state-of-the-art deep-learning based object detection are false positives which occur with overconfident scores. This is highly undesirable in autonomous driving and other critical robotic-perception domains because of safety concerns. This paper proposes an approach to alleviate the problem of overconfident predictions by introducing a novel probabilistic layer to deep object detection networks in testing. The suggested approach avoids the traditional Sigmoid or Softmax prediction layer which often produces overconfident predictions. It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives. The approach is validated on the 2D-KITTI objection detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed approach enables interpretable probabilistic predictions without the requirement of re-training the network and therefore is very practical.

翻译：在自动驾驶应用中的目标检测意味着，对行人及车辆等语义目标的检测与跟踪通常源于城市驾驶环境。当前基于深度学习的先进目标检测面临的主要挑战之一是：假阳性区域常伴随过度置信的评分而产生。出于安全考量，这一现象在自动驾驶及其他关键机器人感知领域极为不利。本文提出一种新颖方法，通过在测试阶段向深度目标检测网络引入概率层，以缓解过度置信预测问题。该方法规避了传统Sigmoid或Softmax预测层（常产生过度置信预测）的局限。实验证明，所提技术在降低假阳性区域置信度的同时，不会影响真阳性区域的检测性能。该方法已在基于YOLOV4和SECOND（激光雷达检测器）的2D-KITTI目标检测数据集上得到验证。该方案无需重新训练网络即可实现可解释的概率预测，因而具有极强的实用性。