Infrared Target Detection using Intensity Saliency and Self-Attention

Infrared target detection is essential for many computer vision tasks. Generally, the IR images present common infrared characteristics, such as poor texture information, low resolution, and high noise. However, these characteristics are ignored in the existing detection methods, making them fail in real-world scenarios. In this paper, we take infrared intensity into account and propose a novel backbone network named Deep-IRTarget. We first extract infrared intensity saliency by a convolution with a Gaussian kernel filtering the images in the frequency domain. We then propose the triple self-attention network to further extract spatial domain image saliency by selectively emphasize interdependent semantic features in each channel. Jointly exploiting infrared characteristics in the frequency domain and the overall semantic interdependencies in the spatial domain, the proposed Deep-IRTarget outperforms existing methods in real-world Infrared target detection tasks. Experimental results on two infrared imagery datasets demonstrate the superiorly of our model.