# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 3 Proposed Method
### Enhanced Low-Level Feature Module (ELFM)  
Infrared small targets typically occupy only a few pixels and lack rich semantic context. Therefore, high-resolution spatial cues--such as sharpness, edge response, and local intensity contrast--are essential for early detection. These cues are predominantly present in the shallow layers of the encoder. However, traditional convolutional pipelines often downsample these early features too aggressively, leading to a loss of spatial resolution and detail that can hinder localization accuracy, especially for small or low-contrast objects. To address this issue, we introduce an Enhanced Low-level Feature Module (ELFM) within the PSHNet architecture, which is specifically designed to preserve and refine shallow-level information to support dense localization in the subsequent decoder stages.  
Figure 1: Overall architecture of the proposed PSHNet framework.  
The ELFM is embedded after the first encoder block and operates on high-resolution shallow features. It consists of the following key components:  
Multi-scale dilated convolutions: To capture features at varying receptive fields without downsampling, ELFM applies a series of 3 \(\times\) 3 convolutions with different dilation rates (e.g., 1, 2, and 4). These layers enable the network to gather spatial context from multiple scales while preserving the resolution of the feature map. The choice of dilation rates (1, 2, and 4) strikes a balance between local and mid-range spatial context aggregation, which is particularly important for small objects that lack strong semantic features. Larger dilation rates were experimentally found to produce fragmented responses or ignore localized contrast.  
Channel-wise attention: Following the multi-scale fusion, a squeeze-and-excitation (SE) block is used to recalibrate feature responses across channels. This mechanism learns to emphasize channels that are more relevant for identifying target-related structures, while suppressing background noise.  
Feature aggregation: The outputs of all branches are concatenated and refined using a \(1\times 1\) convolution to reduce dimensionality and unify the enhanced features. This produces the final output tensor, which is then passed to the decoder via skip connections.  
A schematic of ELFM is shown in Figure 2. This module operates entirely in the spatially high-resolution domain, ensuring that localization-critical features are preserved and enriched. Formally, let \(F\) denote the shallow feature map; the output of the dilated convolution branches is calculated by the following:  
\[F_{k}=\sigma(\text{Conv}_{3\times 3}^{d=k}(X)),k\in\{1,2,4\} \tag{1}\]  
where \(\sigma(\cdot)\) is the ReLU activation and denotes the dilation rate.  
The concatenated feature is as follows:  
\[F_{\text{concat}}=[F_{1},F_{2},F_{4}] \tag{2}\]  
The channel attention weight vector \(\alpha\in\mathbb{R}^{1\times 1\times C^{\prime}}\) is computed via squeeze-and-excitation:  
\[\alpha=\sigma_{s}(W_{2}\cdot\text{ReLU}(W_{1}\cdot\text{GAP}(F_{\text{concat}} ))) \tag{3}\]  
where \(s\) represents the channel-wise attention weights computed via the SE block. The final enhanced feature output is as follows:  
\[F_{\text{ELFM}}=\text{Conv}_{1\times 1}(F_{\text{concat}}\otimes\alpha) \tag{4}\]  
where \(\otimes\) denotes channel-wise multiplication and is the number of concatenated channels.  
Figure 2: Customized SE Block in PSHNet-ELFM.  
ELFM plays a foundational role in PSHNet by ensuring that the heatmap, offset, and size branches receive feature inputs with strong spatial discriminability. The combination of multi-scale dilation and attention improves the robustness of the model in detecting small, low-contrast targets under noisy or cluttered conditions.