Where to look for tiny objects? ROI prediction for tiny object detection in high resolution images

In this paper, we focus on the detection of tiny objects. The goal is to improve the performance of tiny object detection while preserving the average precision and recall metrics compared to a brute-force, sliding-window approach. We extend the object categories in COCO detection metrics from small, medium, and large by defining tiny, very tiny, and micro-objects. We propose an evaluation protocol for all six object sizes. To detect tiny objects, we offer a novel ROI proposal method based on a two-level nested U-structure architecture U
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-Net. For this purpose, we experiment with multiple dilation techniques as well as Region of Interest (ROI) aggregation methods. We evaluate our method using the Mapillary Traffic Sign Dataset. The obtained detection strategy outperforms the single-step prediction approach and is comparable to the quality obtained with the use of the sliding-window, being nearly 7 times faster.