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The current convolution-based semantic segmentation network lacks long-range dependencies for infrared small target detection, which may lead to unsatisfactory detection results in the real scenario. To address the problem, this paper proposed a semantic segmentation network based on hetero-range feature fusion (HRFFnet). Compared with the common semantic segmentation networks, this network includes two feature extraction branches. One is a short-range extraction branch consisting of convolution operations, and the other is a long-range feature extraction branch consisting of transformer. The HRFFnet complements the local features extracted by the convolutional neural network by adding the transformer structure to the segmentation network to introduce the long-range information of image. And this paper also designed a hetero-range fusion module. This module is based on atrous spatial pyramid pooling and adds shortcut connection to fuse different ranges of features extracted from images, which can maintain resolution of image and improve ability of feature representation. The hetero-range fusion module fuses long-range dependencies and short-range information extracted by transformer and convolution to capture multi-scale contextual information about the scene in infrared images and facilitate the interchange of information between different features. To evaluate the HRFFnet, we compare the performance of our network against other high-performance convolution-based methods and transformer-based networks on the open SIRST dataset with different evaluation metrics. The proposed method achieves a better combined results in terms of intersection of Dice coefficient, pixel accuracy, intersection over union and receiver operating characteristic curve. The experiments and results show that the network has some superiority: one is that it can break through the limitation of range of extracting features when only using convolutional network or transformer-based network; the other one is that this network can perform better with good robustness against real and complex scenarios. So, the proposed algorithm has broad application prospects in border patrol and urban security fields.
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