Infrared Small Target Detection (IRSTD) refers to detecting faint targets in infrared images, which has achieved notable progress with the advent of deep learning. However, the drive for improved detection accuracy has led to larger, intricate models with redundant parameters, causing storage and computation inefficiencies. In this pioneering study, we introduce the concept of utilizing network pruning to enhance the efficiency of IRSTD. Due to the challenge posed by low signal-to-noise ratios and the absence of detailed semantic information in infrared images, directly applying existing pruning techniques yields suboptimal performance. To address this, we propose a novel wavelet structure-regularized soft channel pruning method, giving rise to the efficient IRPruneDet model. Our approach involves representing the weight matrix in the wavelet domain and formulating a wavelet channel pruning strategy. We incorporate wavelet regularization to induce structural sparsity without incurring extra memory usage. Moreover, we design a soft channel reconstruction method that preserves important target information against premature pruning, thereby ensuring an optimal sparse structure while maintaining overall sparsity. Through extensive experiments on two widely-used benchmarks, our IRPruneDet method surpasses established techniques in both model complexity and accuracy. Specifically, when employing U-net as the baseline network, IRPruneDet achieves a 64.13% reduction in parameters and a 51.19% decrease in FLOPS, while improving IoU from 73.31% to 75.12% and nIoU from 70.92% to 74.30%. The code is available at https://github.com/hd0013/IRPruneDet.

Infrared small target detection (IRSTD) is widely used in many fields such as detection and guidance systems and is of great research importance. However, small targets in infrared images are typically small, blurry, feature-poor, and prone to being overwhelmed by noisy backgrounds, posing a significant challenge for IRSTD. In this paper, we propose a thermodynamics-inspired multi-feature network (TMNet) for the IRSTD task, which extracts richer and more essential semantic features of infrared targets through cross-layer and multi-scale feature fusion, along with the assistance of a thermodynamics-inspired super-resolution branch. Specifically, it consists of an attention-directed feature cross-aggregation encoder (AFCE), a U-Net backbone decoder, and a thermodynamic super-resolution branch (TSB). In the shrinkage path, the original encoder structure is reconstructed as AFCE, which contains two depth-weighted multi-scale attention modules (DMA) and a cross-layer feature fusion module (CFF). The DMA and CFF modules achieve self-feature-guided multi-scale feature fusion and cross-layer feature interaction by utilizing semantic features from different stages in the encoding process. In thermodynamics, the difference in the formation of different heat between particles leads to heat transfer between objects, which inspired us to analogize the feature extraction process of gradually focusing the network’s attention to an infrared target under the constraints of the loss function to the process of heat transfer. On the expansion path, the TSB module incorporates the Hamming equation of thermodynamics to mine infrared detail features through heat transfer-inspired high-resolution feature representations while assisting the low-resolution branch to learn high-resolution features. We conduct extensive experiments on the publicly available NUAA-SIRSST dataset and find that the proposed TMNet exhibits excellent detection performance in both pixel-level and object-level metrics. This discovery provides us with a relatively dependable guideline for formulating network designs aimed at IRSTD.