Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds
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The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions which 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. To this end, we propose a new 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. Additionally, we design a feature enhancement module to learn more features of small-dim targets. After that, we adopt a decoder with the U-Net-like skip connection operation to get the detection result. Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods.
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