EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929655286595584 |
|---|---|
| author | Liao, Brian Hsuan-Cheng Cheng, Chih-Hong Esen, Hasan Knoll, Alois |
| author_facet | Liao, Brian Hsuan-Cheng Cheng, Chih-Hong Esen, Hasan Knoll, Alois |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union Liao, Brian Hsuan-Cheng Cheng, Chih-Hong Esen, Hasan Knoll, Alois Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics 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. |
| title | EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2403.15474 |