Enhancement-basedSmall TargetDetection for InfraredImages

Abstract: Infrared small target detection is widely used in fields such as military and security. UNet, which is a classical semantic segmentation method proposed in 2015, has shown excellent performance and robustness. However, U-Net suffers from the problem of losing small targets in deep layers after multiple down-sampling operations. Dilated convolution, as a special convolution that can increase the receptive field without increasing the number of parameters, is considered to be able to optimize the problems caused by down-sampling. Dense Nested Attention Network (DNANet), due to its superior performance, was chosen as the baseline, but it still has the issue of target loss. This study proposes three optimization directions: deep down-sampling replaced by cascaded dilated convolution, dilated spatial attention, and dilated residual block. In these three directions, this study proposes four methods, respectively DNANet-DS-1, DNANet-DS-2, DNANet-Att, and DNANet-RB. Two open-source infrared small target datasets, NUDT-SIRST and NUAA-SIRST, were used in this study. The four proposed methods were trained and tested on these two datasets. Among them, DNANetRB significantly outperforms other methods on the NUAA-SIRST dataset, so further experiments were conducted to observe the influence of different network depths on DNANet-RB. The experimental result indicates that when the network depth exceeds a certain threshold, the network can only achieve marginal improvements, but the number of parameters will increase significantly.