IRPruneDeXt: Efficient Infrared Small Target Detection via Musical Wavelet-Regularized Channel Pruning.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40844941.
- Also identified by DOI 10.1109/TNNLS.2025.3594958.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Infrared small target detection (IRSTD) refers to detecting faint targets in infrared (IR) 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 (SNRs) and the absence of detailed semantic information in IR images, directly applying existing pruning techniques yields suboptimal performance. To address this, we propose a novel wavelet structure-regularized multidimensional musical scale soft channel pruning (SCP) method, giving rise to the efficient IRPruneDeXt model. Our approach involves representing the weight matrix in the wavelet domain and formulating a wavelet channel pruning (WCP) strategy. We incorporate wavelet regularization to induce structural sparsity without incurring extra memory usage. Additionally, we design a multidimensional musical scale soft channel reconstruction (MMSCR) method that adapts the strategy across temporal and spatial dimensions to preserve key target information and prevent premature pruning. By leveraging interactions between criteria, it balances pruning and reconstruction through a musical scale feedback effect, achieving an optimal sparse structure while maintaining overall sparsity. Through extensive experiments on many widely used benchmarks, our IRPruneDeXt method surpasses established techniques in both model complexity and accuracy. Specifically, when employing U-net as the baseline network, IRPruneDeXt achieves a 65.68% reduction in parameters and a 51.77% decrease in floating-point operations (FLOPs) while improving intersection over union (IoU) from 73.31% to 76.17% and normalized IoU (nIoU) from 70.92% to 75.08%. The code is available at github.com/hd0013/IRPruneDet.