The small infrared target detection based on visual contrast mechanism

In recent years, the target detection based on Human Visual System (HVS) has been extensively studied by scholars at home and abroad, especially for the visible target detection. Generally, these algorithms involve three processes, the extraction of visual feature according to the targets’ characteristics, the generation of saliency map to segment the targets and the simulation of eye movement to track the targets. Considering the small infrared targets have less information than visible targets, such as color and shape, but they have saliency characteristic of contrast, in this paper, an algorithm of small infrared target detection based on visual contrast mechanism has been proposed. Firstly, according to the targets’ local contrast feature, the saliency map of the image is generated. By calculating the saliency map, it can enhance the targets and suppress the background. Then the targets can be detected after the saliency map is segmented. Considering it is difficult to detect the targets when the image’s signal-to-noise ratio (SNR) is very low even the targets are submerged in the background, the algorithm simulates eye movement to track the targets. The position of the suspicious target is predicted by the algorithm of Proportional-Integral-Derivative (PID). By enhancing the suspicious target’s local region using the Retinex theory and segmenting the local region, the targets can be re-detected. By comparing the proposed algorithm with other methods, the experiments show that the proposed method works well, not only it can be applied to the situation the target is submerged in the background, but it can also be used in different complex background.