The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one-stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. In addition, on the same datasets, the proposed algorithm can reduce the FP by 41.40% and 40.62%, and increase the TP by 7.26% and 4.3%, respectively. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 42 frames per second (fps).

本文提出了一种通过使用高斯参数建模和重新设计损失函数来提高YOLOv3的检测精度以及一种用于预测位置不确定性以指示bbox可靠性的方法。经过KITTI和Berkeley deep drive (BDD)数据集测试，与传统的YOLOv3相比，所提出的算法(Gaussian YOLOv3)在保持实时检测速度(42帧/秒)的同时，将平均精度提高了3.09和3.5个点。因此，本研究提出的算法是自动驾驶应用中最合适的检测算法。

Gaussian YOLOv3：一种使用局部化不确定性准确快速检测自动驾驶对象的方法