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An Evaluation Metric for Object Detection Algorithms in Autonomous Navigation Systems and its Application to a Real-Time Alerting System

机译：自主导航系统中目标检测算法的评估指标及其在实时预警系统中的应用

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摘要

An autonomous navigation system relies on a number of sensors including radar, LIDAR and a visible light camera for its operation. We focus our attention on the visible light camera in this work. Object detection is the key first step to processing the video input from the camera. Specifically, we address the problem of assessing the performance of object detection algorithms in hazardous driving conditions that an autonomous navigation system is expected to encounter in a realistic scenario. To this end, we propose a novel metric for quantifying the degradation in performance of an object detection algorithm under different weather conditions. Additionally’ we introduce a real-time method to detect extreme variations in performance of the algorithm that can be used to issue an alert. We evaluate the performance of our metric and alerting system and demonstrate its utility using the YOLOv2 object detection algorithm trained on the KITTI and virtual KITTI dataset.
机译：自主导航系统依靠许多传感器（包括雷达，LIDAR和可见光摄像头）进行操作。在这项工作中，我们将注意力集中在可见光相机上。对象检测是处理从摄像机输入的视频的关键的第一步。具体来说，我们解决了在危险驾驶条件下评估对象检测算法性能的问题，在这种情况下，自动导航系统有望在现实情况下遇到。为此，我们提出了一种新颖的度量标准，用于量化在不同天气条件下目标检测算法性能的下降。此外，我们引入了一种实时方法来检测可用于发出警报的算法性能的极端变化。我们评估了指标和警报系统的性能，并使用在KITTI和虚拟KITTI数据集上训练的YOLOv2对象检测算法演示了其实用性。

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