Infrared Small UAV Target Detection Based on Depthwise Separable Residual Dense Network and Multiscale Feature Fusion

Abstract

Unmanned aerial vehicles (UAVs) have been widely applied in military and civilian fields, but they also pose great threats to restricted areas, such as densely populated areas and airports. Thermal infrared imaging technology is capable of monitoring UAVs at a long range in both day and night conditions. Therefore, the anti-UAV technology based on thermal infrared imaging has attracted growing attention. However, the images acquired by infrared sensors often suffer from small and dim targets as well as heavy background clutter and noise. Conventional detection methods usually have a high false alarm rate and low detection accuracy. This paper proposes a detection method that formulates the UAV detection as predicting the residual image (i.e., background, clutter, and noise) by learning the nonlinear mapping from the input image to the residual image. The UAV target image is obtained by subtracting the residual image from the input infrared image. The constructed end-to-end U-shaped network exploits the depthwise separable residual dense blocks in the encoder stage to extract the abundant hierarchical features. Besides, the multi-scale feature fusion and representation block is introduced to fully aggregate multi-scale features from the encoder layers and intermediate connection layers at the same scale as well as the decoder layers at different scales to better reconstruct the residual image in the decoder stage. In addition, the global residual connection is adopted in the proposed network to provide long-distance information compensation and promote gradient backpropagation, which further improves the performance in reconstructing the image. The experimental results show that the proposed method achieves favorable detection performance in real-world infrared images and outperforms other state-of-the-art methods in terms of quantitative and qualitative evaluation metrics.