# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 4 Experiment
### Internal Module Evaluation (Ablation Study)  
To thoroughly analyze and validate the contribution of individual components within the proposed PSHNet framework, a detailed ablation study was conducted using the NUDT-SIRST dataset under consistent training and evaluation conditions. Specifically, the experiments aimed to quantify the impact of the following key components: the Enhanced Low-level Feature Module (ELFM), Offset and Size prediction heads, and the CIoU geometric regularization. The baseline model was defined as a standard encoder-decoder network with only the heatmap prediction branch supervised by the modified focal loss.  
We systematically constructed five configurations to assess the incremental contributions of each component:  
* Baseline: U-Net encoder-decoder architecture with only the heatmap branch and modified focal loss supervision.
* Baseline + ELFM: Baseline augmented with the Enhanced Low-level Feature Module to strengthen spatial details.
* Baseline + Offset/Size Heads: Baseline enhanced with offset and size regression heads supervised by L1 loss.
* Baseline + CIoU Regularization: Baseline incorporating CIoU loss to regularize geometric consistency of predictions.
* Full Model (Proposed PSHNet): Complete integration of ELFM, offset/size heads, and CIoU regularization.  
Figure 5: Visual comparison of detection results under varying conditions. Each row corresponds to a test case from the IRSTD-1k dataset. From left to right: Input Image, Ground Truth, Top-Hat, IPI, MDvsFA, ALCNet, DNANet, and PSHNet (Ours). Red circles indicate true targets, green circles indicate missed detections, and yellow circles indicate false alarms.  
The results of the ablation experiments are summarized quantitatively in Table 2, highlighting improvements in Intersection over Union (IoU), Probability of Detection (Pd), and reduction in False Alarm Rate (Fa) as each component was added incrementally.  
As indicated in Table 4, integrating ELFM alone significantly improved IoU from 78.16% to 80.43% and notably reduced the false alarm rate from 27.14 \(\times\) 10\({}^{-6}\) to 20.67 \(\times\) 10\({}^{-6}\), demonstrating the importance of preserving low-level spatial detail for infrared small-target detection. The addition of offset and size heads further refined center localization and bounding box regression accuracy, improving IoU to 80.91% and reducing Fa to 18.53 \(\times\) 10\({}^{-6}\). Furthermore, introducing the CIoU geometric regularization independently enhanced localization precision, boosting IoU to 81.47% and Pd to 98.02%, simultaneously achieving a lower Fa of 17.21 \(\times\) 10\({}^{-6}\).  
Ultimately, the complete PSHNet configuration, which simultaneously incorporates ELFM, offset/size regression, and CIoU regularization, achieved the best overall performance, reaching an IoU of 83.02%, a Pd of 98.94%, and the lowest false alarm rate of 14.08 \(\times\) 10\({}^{-6}\). These results underscore a strong synergy among the designed components, confirming their collective importance in addressing the unique challenges of infrared small-target detection tasks.  
To assess the impact and robustness of data augmentation, we conducted additional experiments by adjusting or removing key augmentation strategies. When all augmentation--including flipping, rotation, and contrast jittering--was disabled, the performance of PSHNet on NUDT-SIRST dropped from 83.02% to 78.31% in IoU and from 98.94% to 95.52% in Pd, highlighting the importance of augmentation in preventing overfitting and improving generalization. Additionally, when only the rotation jitter was intensified (from \(\pm\)30\({}^{\circ}\) to \(\pm\)60\({}^{\circ}\)), the Pd dropped slightly to 98.31% and IoU to 82.47%, indicating that the model remains robust under stronger geometric perturbations.