Fast and Robust Infrared Image Small Target Detection Based on the Convolution of Layered Gradient Kernel

Infrared (IR) small target detection is challenging because the IR imaging lacks detailed features, weak shape features, and a low signal-to-noise ratio (SNR). The existing small IR target detection methods usually focus on improving their high detective performance without considering the execution time. However, high-speed detection is vital for various applications, such as early warning systems, military surveillance, infrared search and track (IRST), etc. This paper proposes a fast and robust single-frame IR small target detection algorithm with a low computational cost while maintaining excellent detection performance. We propose a layered gradient kernel (LGK) based on the contrast properties of the human visual system (HVS) and model it through a three-layer patch image model. The layered gradient kernel is used to convolute with the input IR frame to obtain its gradient map. The target detection is further performed on the acquired gradient map with an adaptive threshold method. This method is compared with eight representative small target detection algorithms to evaluate the performance. Experimental results demonstrate that the algorithm is fast and suitable for real-time applications, and it is very effective even when the small target size is as small as 2×2.