ARFC-WAHNet: Adaptive Receptive Field Convolution and Wavelet-Attentive Hierarchical Network for Infrared Small Target Detection

Infrared small target detection (ISTD) is critical in both civilian and military applications. However, the limited texture and structural information in infrared images makes accurate detection particularly challenging. Although recent deep learning-based methods have improved performance, their use of conventional convolution kernels limits adaptability to complex scenes and diverse targets. Moreover, pooling operations often cause feature loss and insufficient exploitation of image information. To address these issues, we propose an adaptive receptive field convolution and wavelet-attentive hierarchical network for infrared small target detection (ARFC-WAHNet). This network incorporates a multi-receptive field feature interaction convolution (MRFFIConv) module to adaptively extract discriminative features by integrating multiple convolutional branches with a gated unit. A wavelet frequency enhancement downsampling (WFED) module leverages Haar wavelet transform and frequency-domain reconstruction to enhance target features and suppress background noise. Additionally, we introduce a high-low feature fusion (HLFF) module for integrating low-level details with high-level semantics, and a global median enhancement attention (GMEA) module to improve feature diversity and expressiveness via global attention. Experiments on public datasets SIRST, NUDT-SIRST, and IRSTD-1k demonstrate that ARFC-WAHNet outperforms recent state-of-the-art methods in both detection accuracy and robustness, particularly under complex backgrounds. The code is available at https://github.com/Leaf2001/ARFC-WAHNet.

CSPENet: Contour-Aware and Saliency Priors Embedding Network for Infrared Small Target Detection

Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications. Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments, severely limiting their detection performance. To tackle these issues, we propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD. We first design a surround-convergent prior extraction module (SCPEM) that effectively captures the intrinsic characteristic of target contour pixel gradients converging toward their center. This module concurrently extracts two collaborative priors: a boosted saliency prior for accurate target localization and multi-scale structural priors for comprehensively enriching contour detail representation. Building upon this, we propose a dual-branch priors embedding architecture (DBPEA) that establishes differentiated feature fusion pathways, embedding these two priors at optimal network positions to achieve performance enhancement. Finally, we develop an attention-guided feature enhancement module (AGFEM) to refine feature representations and improve saliency estimation accuracy. Experimental results on public datasets NUDT-SIRST, IRSTD-1k, and NUAA-SIRST demonstrate that our CSPENet outperforms other state-of-the-art methods in detection performance. The code is available at https://github.com/IDIP2025/CSPENet.