This paper presents safety-oriented object detection via a novel Ego-Centric Intersection-over-Union (EC-IoU) measure, addressing practical concerns when applying state-of-the-art learning-based perception models in safety-critical domains such as autonomous driving. Concretely, we propose a weighting mechanism to refine the widely used IoU measure, allowing it to assign a higher score to a prediction that covers closer points of a ground-truth object from the ego agent's perspective. The proposed EC-IoU measure can be used in typical evaluation processes to select object detectors with higher safety-related performance for downstream tasks. It can also be integrated into common loss functions for model fine-tuning. While geared towards safety, our experiment with the KITTI dataset demonstrates the performance of a model trained on EC-IoU can be better than that of a variant trained on IoU in terms of mean Average Precision as well.

通过Ego-Centric Intersection-over-Union (EC-IoU)度量，提出一种面向安全的目标检测方法，以解决在自动驾驶等安全关键领域使用先进学习感知模型时的实际问题。在KITTI数据集上的实验结果表明，基于EC-IoU训练的模型在平均准确率方面优于基于IoU训练的变体。

基于自我中心交并比的物体检测器定向安全