Infrared small target detection based on dual-domain and global context feature extraction

Aiming at two inherent problems in single frame infrared small target detection (ISTD): The small target lacks local information such as color, texture and shape; The small targets are readily lost during the continuous down-sampling process that yields high-level semantic information and the global receptive field. A double-domain and global context feature extraction network (DDGC-FENet) that is both precise and quick is suggested. The model includes a dual-domain feature extraction (DDFE) module and a global context feature extraction (GCFE) module. The DDFE module simultaneously learns the local contrast information of the small target and the background in the spatial domain and the frequency domain, so as to separate the target from the background. The GCFE module can globally model the feature map after multiple down-sampling to extract the global context and prevent the loss of target features in the deep layer of the network. Furthermore, the model fuses low-level and high-level features from both row and column directions using a two-way attention fusion (TWAF) module. The suggested approach outperforms cutting-edge techniques like AGPCNet, DNANet, and ISNet in terms of mIoU, nIoU, and F1, according to experiments conducted on a number of public datasets.