Patch-based Selection and Refinement for Early Object Detection
Tianyi Zhang Kishore Kasichainula Yaoxin Zhuo Baoxin Li Jae-Sun Seo Yu Cao
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\%. The
source code is available at
\href{https://github.com/destiny301/dpr}{https://github.com/destiny301/dpr}