Sparsity-Aware Global Channel Pruning for Infrared Small-Target Detection Networks

For infrared small-target detection, convolutional neural network (CNN)-based methods have demonstrated promising performance. However, due to the small size of the targets, existing infrared small-target detection methods necessitate intricate structures with intensive computation to maintain distinctive features of targets in deep layers, which poses great challenges for deployment on edge devices with constrained resources. The current pruning methods predominantly focus on the optimization of classification networks, with an emphasis on semantic information. Nevertheless, the spatial details crucial for infrared small-target detection are excessively pruned in the shallow layers, resulting in a significant degradation of detection performance. In this article, based on the sparse distribution of small targets in infrared images, we propose a sparsity-aware global channel pruning (SAGCP) framework to optimize infrared small-target detection networks. Specifically, sparse modeling is used to code the target region for the first time, and sparse priors can be induced into the feature map for the identification of redundant channels. Without the need for extra structures or intricate criteria to identify redundant channels, our pruning method can leverage the inherent properties of infrared small targets to extract more robust features and obtain more compact models. When SAGCP is applied to the existing infrared small-target detection methods, the pruned network is superior in model efficiency and detection performance. For example, when applying our method to DNA-Net, the pruned model can achieve a 72.34% reduction in parameters, and a 57.49% decrease in floating point operations (FLOPs), but a 2.02% increase in intersection over union (IoU).