## 4 Experimental Results
### Experimental Results
#### 4.2.4 Effect on Object-Detection Performance  
Deraining of images captured by AVs in real driving scenarios is considered an effective enhancement of images quality which subsequently enhances the performance of object-detection task performed by AVs, which is the main objective of our work. Therefore, we investigated the effect of applying deraining algorithms, including our proposed network, on the accuracy of object detection. In order to perform this evaluation, we used samples collected by [PERSON] et al. in [44]. They have collected samples from datasets that include driving scenes with diverse conditions (850 samples from COCO and BDD datasets to create new synthetic rain datasets: COCO350, and BDD350), such as diverse rain orientations and magnitudes, in addition to complex scenes (e.g., night scenes). First, we applied our proposed deraining algorithm, as well as other SOTA deraining algorithms in order to restore rain-free images. Afterwards, we apply the object-detection framework--YOLOv3--to assess the detection efficiency on de-rained images. Quantitative results of both the deraining performance as well as the detection efficiency are shown in Table 6. In addition, visual comparisons are shown in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Evaluation Metrics** & **HINet** & **MPRNet** & **MSPFN** & **PreNet** & **Proposed** \\ \hline \multirow{2}{*}{RID} & NIQE \(\downarrow\) & 4.985 & 4.856 & 6.518 & 7.007 & 4.562 \\  & SSEQ \(\downarrow\) & 81.162 & 50.648 & 40.47 & 43.04 & 48.213 \\ \multirow{2}{*}{RIS} & NIQE \(\downarrow\) & 6.887 & 6.045 & 6.135 & 6.722 & 6.148 \\  & SSEQ \(\downarrow\) & 52.983 & 51.689 & 43.47 & 48.22 & 52.681 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of average NIQE/SSEQ on real-world dataset (RID and RIS). The smaller scores indicate better perceptual quality. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Size** & **HINet** & **MPRNet** & **MSPFN** & **PreNet** & **Proposed** \\ \hline \(500\times 500\) & 0.574 & 0.792 & 0.415 & 0.464 & 0.251 \\ \(1024\times 1024\) & 2.065 & 2.782 & 1.538 & 1.578 & 0.843 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of running time (sec) on NVIDIA GTX 1050 Ti GPU. The object-detection accuracy is greatly degraded by the presence of unclear weather conditions, especially rain. Rain drops cover and distort underlying visual features of surrounding objects, which are used by detection methods to classify and localize objects. Figure 10 demonstrates the performance of object detection with and without draining in advance. Not all deraining algorithms boosts the performance of object detection; however, [PERSON] et al. in [70] and [PERSON] et al. in [69] have shown that some deraining algorithms [43; 51] may degrade the detection performance when compared with directly using the rainy images as input to the corresponding detection frameworks, as they are not optimized towards the end goal of object detection. One factor for this degradation is that the tested deraining algorithms tended to smooth out the images, and hence, distorts important visual features of a scene, and smooths out edges of objects leading to missing detecting them. Therefore, in our research it is crucial to evaluate the performance of our proposed deraining model by jointly evaluating the object-detection performance on the de-rained output. As shown in Table 6, the detection precision of the produced de-rained images by our proposed model shows noticeable improvement compared with that of original rainy inputs by 68%. Additionally, when compared with other SOTA deraining models, the de-rained images generated by our proposed model shows more dependable contents with clearer features, which efficiently boosts the detection performance.