A double-layer feature fusion convolutional neural network for infrared small target detection

Infrared small target detection is critical in remote sensing, military, and other fields. However, the low resolution of most infrared images and the lack of texture and detailed information could cause the target to be lost in a relatively noisy background. Therefore, in recent years, researchers have paid particular attention to the problem of small infrared target detection. In this paper, we propose a double-layer feature fusion convolutional neural network for infrared small target detection (DLFF), consisting of a simultaneous upsampling two-layer network module and a 'T'-type fusion structure. First, the upsampling double-layer network module shares detection information while synchronizing detection, suppressing the background noise and enhancing the detection of the target. In addition, for the small target detection task, since the direct fusion of shallow spatial information and deep semantic information may lose only some small target features, we propose a 'T'-type fusion structure to solve this problem. Furthermore, we collate an infrared small target dataset (MDFA_SIRIST) and design a pre-processing method for pre-detection images. The experimental results show that our network outperforms the other six state-of-the-art methods in combined evaluation metrics (F1-score) and mean intersection ratio (mIou).