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Infrared small target detection is widely used in precision guided weapons and early warning systems. It is very difficult to detect small target in infrared image because the target is small in size and weak in texture information. The traditional methods can’t make full use of the characteristics of the target which results in a low detection rate and a high false alarm rate, and do not consider the complexity of the algorithm which makes it difficult to apply to systems with high real-time requirements. This letter proposes a method that combines hand-designed features and machine-designed features for fast and accurate infrared small target detection. First of all, the region of interest is extracted by the proposed local weighted intensity difference method and local eight-direction gradient method. Then, the detection decision map is constructed to improve the detection speed and compose the decomposed target. Finally, the non-translation invariant CoordConv is used to detect target in the region of interest, which solves the defect that the translation invariance of traditional convolution loses the hand-designed features in the region of interest. Numerous experimental results demonstrate our method has the best detection performance compared with baseline methods, while keeps the lowest detection time.

Fast and robust infrared small target detection based on a combination of hand-designed features and machine-designed features