Object Detection Algorithm of UAV Aerial Photography Image Based on Anchor-Free Algorithms

Abstract

Aiming at the problems of the difficult extraction of small target feature information, complex background, and variable target scale in unmanned aerial vehicle (UAV) aerial photography images. In this paper, an anchor-free target detection algorithm based on fully convolutional one-stage object detection (FCOS) for UAV aerial photography images is proposed. For the problem of complex backgrounds, the global context module is introduced in the ResNet50 network, which is combined with feature pyramid networks (FPN) as the backbone feature extraction network to enhance the feature representation of targets in complex backgrounds. To address the problem of the difficult detection of small targets, an adaptive feature balancing sub-network is designed to filter the invalid information generated at all levels of feature fusion, strengthen multi-layer features, and improve the recognition capability of the model for small targets. To address the problem of variable target scales, complete intersection over union (CIOU) Loss is used to optimize the regression loss and strengthen the model’s ability to locate multi-scale targets. The algorithm of this paper is compared quantitatively and qualitatively on the VisDrone dataset. The experiments show that the proposed algorithm improves 4.96% on average precision (AP) compared with the baseline algorithm FCOS, and the detection speed is 35 frames per second (FPS), confirming that the algorithm has satisfactory detection performance, real-time inference speed, and has effectively improved the problem of missed detection and false detection of targets in UAV aerial images.