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arXiv · 2403.15474

EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union

Brian Hsuan-Cheng Liao·Chih-Hong Cheng·Hasan Esen·Alois Knoll
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Abstract

This paper presents Ego-Centric Intersection-over-Union (EC-IoU), addressing the limitation of the standard IoU measure in characterizing safety-related performance for object detectors in navigating contexts. Concretely, we propose a weighting mechanism to refine IoU, 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 better 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.

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Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll. 2024-03-20. EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union. https://doi.org/10.1109/iros58592.2024.10801740

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