TinyWT: A Large-Scale Wind Turbine Dataset of Satellite Images for Tiny Object Detection

Mingye Zhu, Zhicheng Yang, Hang Zhou, Chen Du, Andy Wong, Yibing Wei, Zhuo Deng, Mei Han, Jui-Hsin Lai; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 794-804

Tiny object detection is a challenging task. Many datasets for this task are released in past years, spanning from natural scene to remote sensing images. However, wind turbines in satellite images, a significant category of tiny objects, have not been well included. Aiming at completing the tiny object datasets, we release TinyWT, a large-scale year-round tiny wind turbine dataset of satellite images. It has 8k+ images, a very tiny object size of 3-6 pixels, and 700k+ annotations in total with the extensive effort of human correction. Unlike other tiny object datasets of aerial/satellite images that are limited to academic research only, our dataset is free for commercial use. Every pixel's geographic coordinates are also explicitly extracted for researchers without related domain knowledge. Meanwhile, we reposition the tiny object detection task as a localizing-and-counting problem and incorporate segmentation techniques, and propose a novel design to exploit the strengths of contextual similarity constraint and supervised contrastive learning. The experiment results of both baseline models (CNN-based and Transformer-based models) as well as our special design are presented. Without bells and whistles, our design effectively improves the baseline models' performance, achieving a maximum of 4.94% mIoU gain where 21.15% of false negatives are recalled and 22.02% of false positives are removed. TinyWT is available on https://github.com/MingyeZhu123/TinyWT-dataset.