EC-IoU: 自己中心的な交差オーバーユニオンによる物体検出器の安全性指向
EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union
自動運転などのナビゲーション文脈で物体検出器の安全性を評価するため、自己中心的な視点から近い点を重視するEC-IoU指標を提案し、KITTIデータセットで有効性を示した。
著者: Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
