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.
Highlights
The development of vehicle detection technology in remote sensing images makes it possible to obtain traffic information in time, which is significant for road traffic monitoring, management and scheduling applications
The detection head predicts the category of each pixel in the output heatmap and the position offset of keypoints
The spatial resolution ranges from 0.1 m to 0.3 m. We resampled these high-resolution remote sensing images with a downscaling factor of 5, so that the number of vehicle object pixels is less than 80, which conforms to the definition of small-sized objects in this paper
Summary
The development of vehicle detection technology in remote sensing images makes it possible to obtain traffic information in time, which is significant for road traffic monitoring, management and scheduling applications. The detection of small-sized vehicle in remote sensing images is a difficult problem. The current literature for vehicle detection in remote sensing images can be divided into descriptor-based and feature-learning-based methods. Object features are affected by background features, which makes it more difficult to detect specific features in remote sensing images, and the Remote Sens. Anchor-based methods need to cover the object well, by setting the bounding box in advance, which makes it possible for a few pixels to produce large errors in small-sized objects annotation. It is especially difficult to judge whether the pixels near the bounding box belong to the object Aimed at solving these problems, in this paper, we regard the small object as a keypoint in the relevant background and propose an anchor-free vehicle detection network (AVD-kpNet).
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