Abstract
Recently, unmanned aerial vehicles (UAV) are widely used in many fields due to the low cost and high flexibility. One of the most popular applications of UAV is vehicle detection in aerial images which plays an important role in traffic surveillance and urban planning. Although, many deep learning based detectors have achieved state-of-the-art (SOTA) performance in natural images, the significant variation in object scales caused by the altitude change of the UAV platform brings great challenges to these detectors for precise localization of vehicles in aerial images. To improve the detection performance for vehicles with different scales, we propose a novel detection algorithm which consists of three stages. In the first stage, to reduce the distortion of vehicles during image resizing and keep more information of aerial images, we utilize an image cropping strategy to divide the image into two patches. In the second stage, we combine the original image and two patches into a batch and detect vehicles with a Convolutional Neural Network (CNN). For feature representation in our detector, we propose Scale-specific Prediction to strengthen the multi-scale features of vehicles with context information. In the final stage, to fuse detections and suppress false alarms, we propose an Outlier-Aware Non-Maximum Suppression. Extensive experiments are conducted to demonstrate the superiority of the proposed algorithm by comparison with other SOTA solutions.
Highlights
T HE usage of unmanned aerial vehicles (UAV) is increasing rapidly recent years in plant protection [1], [2], traffic surveillance [3]– [5], disaster rescue [6], and urban planning [7]
For images captured from high-altitude UAVs, the ground sampling distance (GSD) is usually higher than 0.1 meter and the size of a standard vehicle is typically with 48 × 16 pixels [7]
To tackle the problems in vehicle detection in highresolution UAV images, we propose a novel algorithm which mainly consists of three steps
Summary
T HE usage of UAVs is increasing rapidly recent years in plant protection [1], [2], traffic surveillance [3]– [5], disaster rescue [6], and urban planning [7]. The distribution of vehicles in different districts can provide essential information for traffic supervision and urban planning. For images captured from high-altitude UAVs, the ground sampling distance (GSD) is usually higher than 0.1 meter and the size of a standard vehicle is typically with 48 × 16 pixels [7]. For images captured from low-altitude UAVs, the GSD may reach the centimeter level and the typical size of a vehicle is 180 × 80 pixels [13]. Learning an effective representation of vehicles with different scales is an essential challenge for vehicle detection in aerial images
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