# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 4 Results  
### Performance of the Proposed SR Module  
We compare the super-resolution reconstruction results of the proposed SR module with those of the other popular SR methods. According to the fact that our main purpose of this paper is helmet detection instead of pure super-resolution reconstruction, we choose  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Metrics** & **Definition** \\ \hline \multirow{5}{*}{SR} & PSNR & \(\text{PSNR}=10\log_{10}\frac{255}{\frac{1}{\sum_{\text{c}=(\text{RC},\text{S})} \left(\text{MSE}\right)_{\text{c}}}}\) where MSE indicates \\  & & the mean square error of the image. \\ \cline{2-3}  & & \(\text{SSIM}(I_{0},I)=\frac{\left(2I_{0}I_{1}+c_{1}\right)\left(2I_{0}I_{1}+c_{ 2}\right)}{\left(I_{0}^{2}+I_{1}^{2}+I_{1}^{2}\right)\left(I_{0}^{2}+I_{1}^{2} \right)\left(I_{0}^{2}+I_{1}^{2}\right)\left(I_{0}^{2}+I_{1}^{2}\right)}}\) where \(I_{0}\) and \(I\) are \\ \cline{2-3}  & & the original and the reconstructed high-resolution images. \\  & & \(\mu\) and \(\sigma\) indicate mean and variance, respectively, and \(c_{1}\), \(c_{2}\) are constants \\ \hline \multirow{5}{*}{Detection} & Precision & \(\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{TP}}\) where TP, FP and FN indicate true positives, false positives and false negatives, respectively. \\ \cline{2-3}  & Recall & \(\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\) \\ \cline{1-1} \cline{2-3}  & AP & Area under the precision–recall curve \\ \hline \hline \end{tabular}
\end{table}
Table 1: The definition of utilized quantitative metrics.  
the widely used interpolation methods for comparison since they are often utilized to adjust the network input resolution in object detection tasks. Our method starts from the DRN-S model designed for SR tasks so that it is necessary to compare our improvements with the original DRN-S model [30]. The achieved results are shown in Table 2. There is a gap between the PSNR values of SR-based methods and those of interpolation-based methods. Our SR module achieves a higher PSNR value while keeping its SSIM value consistent with that of DRN-S. This means that we can achieve better image quality so as to improve the accuracy of the coming detection module.  
From Figure 4, we can review the super-resolution reconstructed images directly. Since our real targets are helmets, we focus on them more instead of on the background information. All helmet regions are zoomed in to visualize their details. It can be found that the helmets obtained from super-resolution reconstructed-based methods are clearer than those achieved by the interpolation-based method. To be specific, our SR module produces images that are less blurry but not so piecewise smooth to be able to obtain more median-frequency information.  
Based on Table 2, there is a significant improvement using our method compared with the original DRN-S. To evaluate the effort of our newly added contextual loss, we compared the performance of the SR module with different weight \(\alpha\), as shown in Figure 5. DRN-S refers to the original DRN-S model. C-0.001 refers to the SR model trained under the combined loss described in Equation (1) with \(\alpha=0.001\). C-0.0005 and C-0.0001 indicate \(\alpha=0.0005\) and \(\alpha=0.0001\), respectively.