EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union
<|reference_start|>EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union: 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.<|reference_end|>
@article{liao2024ec-iou:,
    title={EC-IoU: Orienting Safety for Object Detectors via Ego-Centric
  Intersection-over-Union},
    author={Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll},
    journal={arXiv preprint arXiv:2403.15474},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15474},
    primaryClass={cs.CV cs.AI cs.LG cs.RO}
}