Patch-Based Selection and Refinement for Early Object Detection
Abstract
Early object detection (OD) is a crucial task for the safety of many dynamic systems. Current OD algorithms have limited success for small objects at a long distance. To improve the accuracy and efficiency of such a task, we propose a novel set of algorithms that divide the image into patches, select patches with objects at various scales, elaborate the details of a small object, and detect it as early as possible. Our approach is built upon a transformer-based network and integrates the diffusion model to improve the detection accuracy. As demonstrated on BDD100K, our algorithms enhance the mAP for small objects from 1.03 to 8.93, and reduce the data volume in computation by more than 77%.
Cite
Text
Zhang et al. "Patch-Based Selection and Refinement for Early Object Detection." Winter Conference on Applications of Computer Vision, 2024.Markdown
[Zhang et al. "Patch-Based Selection and Refinement for Early Object Detection." Winter Conference on Applications of Computer Vision, 2024.](https://mlanthology.org/wacv/2024/zhang2024wacv-patchbased/)BibTeX
@inproceedings{zhang2024wacv-patchbased,
title = {{Patch-Based Selection and Refinement for Early Object Detection}},
author = {Zhang, Tianyi and Kasichainula, Kishore and Zhuo, Yaoxin and Li, Baoxin and Seo, Jae-Sun and Cao, Yu},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2024},
pages = {729-738},
url = {https://mlanthology.org/wacv/2024/zhang2024wacv-patchbased/}
}