基于多层多向 Transformer 的红外弱小目标检测

The convolution neural network based infrared small target detection suffers from the problems of limited receptive field of convolution kernel，information loss caused by down sampling operation，and limited power of the convolution neural network in relative information extraction. To solve these problems，a multi-layer multi-direction Transformer based neural network is proposed. Firstly，the Transformer block is adopted as the basic operator since it has a larger receptive field and more powerful in extracting relative information. The proposed network is a U-shaped network，and fuses local and global information with multi-layers structure. Meanwhile，to enhance the network’s ability to detect the infrared small target，a dual-direction attention operator which calculates the attention information along spatial and channel directions is designed for the decoder network. Finally，an additional network is added to the backbone network to calculate the number of the detected infrared small targets. This additional network reduces the number of falsely detected targets by comparing the calculated number with ground truth. The proposed method is tested on several datasets and the evaluation metrics in comparison with state-of-the-art methods. The proposed method achieves an improvement by 35% at most，which proves the effectiveness of the proposed method.