  The infrared small-dim target detection is one of the key techniques in the
infrared search and tracking system. Since the local regions similar to
infrared small-dim targets spread over the whole background, exploring the
interaction information amongst image features in large-range dependencies to
mine the difference between the target and background is crucial for robust
detection. However, existing deep learning-based methods are limited by the
locality of convolutional neural networks, which impairs the ability to capture
large-range dependencies. Additionally, the small-dim appearance of the
infrared target makes the detection model highly possible to miss detection. To
this end, we propose a robust and general infrared small-dim target detection
method with the transformer. We adopt the self-attention mechanism of the
transformer to learn the interaction information of image features in a larger
range. Moreover, we design a feature enhancement module to learn discriminative
features of small-dim targets to avoid miss detection. After that, to avoid the
loss of the target information, we adopt a decoder with the U-Net-like skip
connection operation to contain more information of small-dim targets. Finally,
we get the detection result by a segmentation head. Extensive experiments on
two public datasets show the obvious superiority of the proposed method over
state-of-the-art methods and the proposed method has stronger cross-scene
generalization and anti-noise performance.
