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 is false positive which occurrences 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 enabling interpretable probabilistic predictions without the requirement of re-training the network and therefore is very practical.

翻译：在自主驾驶应用中检测物体意味着检测和跟踪语义物体通常都是城市驾驶环境所特有的,作为行人和车辆。在以深层次学习为基础的最先进的物体探测中,主要挑战之一是虚伪的正数,这与过于自信的分数有关。由于安全考虑,这在自主驾驶和其他关键的机器人感知领域极不可取。本文件提出一种办法,通过对测试中的深点物体探测网络引入新的概率层来缓解过于自信的预测问题。所建议的办法避免了通常产生过度自信预测的传统Sigmoid或Softmax预测层。事实证明,拟议的技术在不降低真实正数表现的情况下降低了对假正数的自信。该办法在通过YOLOV4和II(基于Lidar的探测器)进行的2D-KITTI反对检测上得到验证。拟议办法使可解释的概率预测无需对网络进行再培训,因此非常实用。