Small-Sized Vehicle Detection in Remote Sensing Image Based on Keypoint Detection

Abstract

The vehicle detection in remote sensing images is a challenging task due to the small size of the objects and interference of a complex background. Traditional methods require a large number of anchor boxes, and the intersection rate between these anchor boxes and an object’s real position boxes needs to be high enough. Moreover, the size and aspect ratio of each anchor box need to be designed manually. For small objects, more anchor boxes need to be set. To solve these problems, we regard the small object as a keypoint in the relevant background and propose an anchor-free vehicle detection network (AVD-kpNet) to robustly detect small-sized vehicles in remote sensing images. The AVD-kpNet framework fuses features across layers with a deep layer aggregation architecture, preserving the fine features of small objects. First, considering the correlation between the object and the surrounding background, a 2D Gaussian distribution strategy is adopted to describe the ground truth, instead of a hard label approach. Moreover, we redesign the corresponding focus loss function. Experimental results demonstrate that our method has a higher accuracy for the small-sized vehicle detection task in remote sensing images compared with several advanced methods.

Keywords

• vehicle detection
• keypoint detection
• Gaussian label
• deep learning
• remote sensing images