A Feature Enhancement and Augmentation-Based Infrared Small Target Detection Network

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Abstract
Infrared small target detection (IRSTD) is the pivotal technology for remote surveillance and search missions under infrared imagery. Due to the target’s nature of being small, dim, and lacking texture information, detection algorithms for relatively large visible targets are not suitable for infrared small targets. In this letter, a real-time, robust IRSTD network with feature enhancement and augmentation, FEA-Net, is proposed, which includes a hybrid multiscale enhancement (HME) module and a saliency detection network with a small target feature augmentation (STFA) module. The HME introduces a hybrid multiscale spatial attention mechanism to obtain a rough estimation of targets and then apply it to the original image. The STFA is directed against the insufficiency of target-related features and the consequent feature losses, which screens out the crucial information and represents it into a larger tensor for further feature fusion, increasing the proportion of target-related features of the output. The results show that the proposed method can achieve the same level of detection effect as the comparable methods and still has considerable real-time performance with the normalized intersection over union (nIoU) of 76.12% on the SIRST dataset and 92.28% on the NUDT-SIRST dataset.

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