M4Net: Multi-level multi-patch multi-receptive multi-dimensional attention network for infrared small target detection.

Abstract

The detection of infrared small targets is getting more and more attention, and has a wider application in both military and civilian fields. The traditional infrared small target detection methods heavily rely on the setting of manual features, and the deep learning-based method easily lose the targets in deep layers due to several downsampling operations. To handle this problem, we design multi-level multi-patch multi-receptive multi-dimensional attention network (M4Net) to achieve information interaction among high-level and low-level features for maintaining target contour and location detail. Multi-level feature extraction module (MFEM) with multilayer vision transformer (ViT) is introduced under the encoder-decoder framework to fuse multi-scale features. Multi-patch attention module (MPAM) and multi-receptive field module (MRFM) are proposed to capture and enhance the feature information. Multi-dimension interactive module (MDIM) is designed to connect the attention mechanism on multiscale features to enhance the network's leaning ability. Finally, the extensive experiments carried out on infrared small target detection dataset demonstrate that our method achieves better performance compared to other methods.