# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 5 Conclusions and Discussion  
This paper introduces PSHNet, a hybrid deep-learning framework designed for accurate and robust infrared small-target detection. By integrating dense heatmap supervision, sub-pixel offset and size regression, and CIoU-based geometric regularization, PSHNet effectively addresses key challenges such as weak thermal signals, small target sizes, and complex background clutter. The inclusion of the Enhanced Low-level Feature Module (ELFM) further enhances the model's sensitivity to fine spatial details. Extensive experiments on two benchmark datasets confirm the effectiveness and robustness of the proposed method. Inference tests on embedded platforms further demonstrate the model's applicability for near real-time deployment.  
While PSHNet demonstrates strong performance under diverse infrared imaging conditions, several limitations remain. First, the model contains 5.7 million parameters and is trained with only a few thousand labeled samples, which may risk overfitting in data-scarce scenarios. Although early stopping was not triggered in our experiments, and no severe overfitting was observed, future work may explore parameter-efficient  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **IoU (\%) \(\uparrow\)** & **Pd (\%) \(\uparrow\)** & **Fa (\(\times\)10\({}^{-6}\)) \(\downarrow\)** \\ \hline Baseline & 78.16 & 96.85 & 27.14 \\ Baseline + ELFM & 80.43 & 97.32 & 20.67 \\ Baseline + Offset/Size Heads & 80.91 & 97.84 & 18.53 \\ Baseline + CIoU Regularization & 81.47 & 98.02 & 17.21 \\
**Full Model (Proposed PSHNet)** & **83.02** & **98.94** & **14.08** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results of the ablation study on the NUDT-SIRST dataset. \(\uparrow\) indicates higher values are better; \(\downarrow\) indicates lower values are better.  
variants or pruning strategies to improve training-data efficiency. Second, detection relies on thresholding the predicted heatmap responses; although this is standard practice in regression-based detectors, confidence calibration is implicit. Practical deployment can adjust this threshold based on ROC analysis or application-specific Pd-Fa trade-offs.  
Moreover, failure cases occasionally arise under extreme low-SNR conditions, partial occlusions, or thermal clutter with structured backgrounds. These scenarios reveal the limitations of purely spatial representations and suggest that additional contextual or temporal information may be beneficial. While the current framework is trained under fully supervised settings, generalization may be further limited in environments with sparse or noisy annotations.  
To address these limitations, future work will explore strategies to enhance adaptability and scalability. Potential directions include the following:  
* Semi-supervised or weakly supervised learning, to reduce dependency on dense labels;
* Model compression, such as replacing deeper convolutional blocks with ghost feature modules [24], to support deployment on low-power platforms;
* Lightweight attention mechanisms [25], to better capture subtle thermal features with minimal computational cost;
* Temporal modeling or multimodal fusion, such as visible-infrared integration, to improve robustness under motion blur or complex scenes.  
Expanding evaluation to real-world video sequences and additional sensor modalities will also be essential steps toward practical and reliable deployment.  
Conceptualization, W.C.; methodology, W.C.; software, W.C.; formal analysis, C.Z.; writing--original draft preparation, W.C.; writing--review and editing, Y.L.; supervision, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used to support the results of this study are included in the article.  
The authors declare no conflicts of interest.