Object detection is a key function in machine perception. Usually, their performance is evaluated based on accuracy metrics such as mean Average Precision (mAP). In this paper, we examine object detectors by their safety in the context of Autonomous Driving (AD). More concretely, we find mAP, which in turn employs the Intersection-over-Union (IoU) measure, not particularly suitable for the notion of safety in AD. Instead, we propose a novel safety metric as a more direct safety reflector, using the Intersection-over-Ground-Truth (IoGT) measure and a distance ratio between predictions and ground truths. We also formulate a safety-aware loss function that can improve an object detector and significantly reduce its unsafe predictions, compared to ordinary ones such as the SmoothL1 loss. Our experiments with open-sourced models and two datasets demonstrate the validity of our consideration and proposals.

翻译：目标检测是机器感知中的关键功能。通常，其性能通过平均精度（mAP）等准确率指标进行评估。本文从自动驾驶（AD）安全性的角度审视目标检测器。具体而言，我们发现当前采用交并比（IoU）度量的mAP并不特别适用于AD中的安全概念。为此，我们提出一种新型安全度量，通过采用交集与真实值之比（IoGT）度量以及预测框与真实框之间的距离比值，作为更直接的安全反映指标。我们还构建了一个安全感知损失函数，相较于SmoothL1损失等常规损失函数，该函数能有效提升目标检测器性能并显著减少不安全预测。基于开源模型和两个数据集的实验验证了我们的思考与提议的有效性。