# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 4 Experiment
### Comparison with State-of-the-Art Methods  
To further validate the effectiveness of PSHNet, we conducted comparative experiments against a range of representative infrared small-target detection algorithms, including both traditional approaches and state-of-the-art deep learning-based models. The traditional baselines include Top-Hat, a morphological filter that enhances bright regions, and IPI, a low-rank decomposition method that separates targets from structured backgrounds. Among learning-based methods, we compared with MDvsFA, which leverages adversarial learning to balance false alarms and missed detections, ALCNet, which applies local contrast attention to enhance small targets, and DNANet, which incorporates nested attention for deep feature fusion. In contrast, our proposed PSHNet unifies dense heatmap-based localization with geometric regularization via CIoU, reinforced by shallow feature enhancement through the ELFM module. All models were retrained on the NUDT-SIRST and IRSTD-1k datasets using identical preprocessing procedures and data splits to ensure a fair and reproducible comparison. Table 1 summarizes the results across the three evaluation metrics. On IRSTD-1k, PSHNet achieves the highest IoU (66.47%) and lowest Fa (6.83 \(\times\) 10\({}^{-6}\)), while maintaining a high Pd of 98.01%. On NUDT-SIRST, our method again leads with an IoU of 83.02% and a Pd of 98.94%, outperforming all baselines with a notably reduced false alarm rate of 14.08 \(\times\) 10\({}^{-6}\). These results demonstrate that PSHNet effectively captures weak, small-scale targets while suppressing background interference, validating the benefit of our hybrid design. Although the Pd score on NUDT-SIRST reaches 98.94%, we emphasize that the official dataset split was strictly followed, and no image or annotation overlaps exist between training and testing. In addition, to assess performance robustness, we computed 95% confidence intervals via bootstrapping (1000 iterations) over the test set. The resulting intervals are: NUDT-SIRST--Pd: 98.94% (\(\pm\)0.68%), IoU: 83.02% (\(\pm\)0.91%); IRSTD-1k--Pd: 98.01% (\(\pm\)0.74%), IoU: 66.47% (\(\pm\)1.08%). To further explore the balance between sensitivity and specificity, Figure 4 presents target-level receiver operating characteristic (ROC) curves on the NUDT-SIRST dataset. PSHNet consistently maintains superior detection probability across a wide range of false alarm thresholds, showing particular advantage in the low-Fa regime critical for practical surveillance applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dataset** & **IoU (\%)**\(\uparrow\) & **Pd (\%)**\(\uparrow\) & **Fa (\(\times\)10\({}^{-6}\))**\(\downarrow\) \\ \hline Top-Hat & & 12.56 & 68.94 & 188.78 \\ IPI & & 26.01 & 70.39 & 36.69 \\ MDvsFA & & 52.88 & 86.90 & 31.32 \\ ALCNet & & 48.02 & 93.30 & 29.87 \\ DNANet & & 63.01 & 97.70 & 9.72 \\ Ours & & 66.47 & 98.01 & 6.83 \\ \hline Top-Hat & & 22.34 & 70.54 & 95.37 \\ IPI & & 18.67 & 67.87 & 45.78 \\ MDvsFA & & 52.84 & 82.34 & 50.55 \\ ALCNet & & 78.45 & 96.34 & 35.99 \\ DNANet & & 82.17 & 98.93 & 23.65 \\ Ours & & 83.02 & 98.94 & 14.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection performance comparison on NUDT-SIRST and IRSTD-1k datasets. \(\uparrow\) indicates higher values are better; \(\downarrow\) indicates lower values are better. Figure 4: Target-level ROC curves on the NUDT-SIRST dataset. Our method consistently achieves a better balance between Pd and Fa across a wide range of confidence thresholds. To assess real-world deployability, we also report GFLOPs and inference time on both high-end and embedded platforms. For reference deployment, we converted PSHNet to TensorRT-FP16 and benchmarked it on an NVIDIA Jetson Orin Nano (6 C CPU @ 1.5 GHz, 512-core GPU). With an input resolution of 256 \(\times\) 256 and batch size = 1, the network runs at 13.4 ms per image and contains 5.7 M parameters. CPU-only inference on an Intel i7-12700K yielded \(\sim\)380 ms per image.