EC-IoU: Orienting Safety for Object Detectors via Ego-Centric
  Intersection-over-Union
Brian Hsuan-Cheng Liao Chih-Hong Cheng Hasan Esen Alois Knoll
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.