Flying Small Target Detection in Infrared Images Based on Adaptive Top-Hat Transform

Automatic detection and tracking of small target in infrared images is the great importance in the modern world. Top-Hat transformation is the important class of nonlinear transformation morphology that has wide usages in the detection and tracking the target in infrared images. The most important problem in improving the efficiency of conversion of Top-Hat transformation, the use of structural elements according to the SNR of each image. Since the images have different clutter and targets, the use of structural elements with fixed shape and dimations for images with different SNR cannot lead to accurate detection. Therefore, to improve the detection, adaptive structural elements should be used. For this purpose, in this paper, we use continuous genetic algorithm to achieve Top-Hat transformation adaptive structural elements, in order to identify more precisely target point. Qualitative and quantitative evaluation results on real images show that the proposed method compared with other methods based on morphology with fixed structural element has a better performance in target detection in infrared image and produce fewer false alarms.