Small Infrared Target Detection Based on Fast Adaptive Masking and Scaling With Iterative Segmentation

Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.