From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images

Abstract

Accurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the image and detecting the objects around those candidate positions. By postponing the object prediction to after increasing its resolution, the obtained key-points are more stable than their traditional counterparts based on early object detection with less visual information. This hierarchical approach, HSOD-Net, saves significant run-time, which makes it more suitable for practical applications such as search and rescue, and drone navigation. In comparison with the state-of-art models, HSOD-Net achieves remarkable precision in detecting small objects in low-resolution remote sensing images.