# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 5 Experiments and Results
### Image Dehazing Result  
In the first experiment, the performance of the proposed image dehazing model was evaluated and compared with other methods using two datasets. The first dataset was a combination of NYU-Depth V2 [38] and RESIDE-\(\beta\)[39]. NYU-Depth V2 contains indoor scenes with synthetic heavy haze, while RESIDE-\(\beta\) contains outdoor scenes with synthetic light haze. There were 2800 images in total (1424 from NYU-Depth V2 and 1376 from RESIDE-\(\beta\)). A total of 2500 images were taken as the training set. The remaining 300 images were used as the test set. To further evaluate the performance of our model on outdoor images, we also utilized the Synthetic Objective Testing Set (SOTS) which is a subset of RESIDE. It contains 500 pairs of real outdoor images with and without synthetic haze. We adopted three evaluation metrics--the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and a score which was a weighted sum of the PSNR and SSIM:  
\[Score=W_{PSNR}\times PSNR+W_{SSIM}SSIM \tag{8}\]  
where \(W_{PSNR}\) and \(W_{SSIM}\) were set to 0.05 and 1, respectively. Table 4 shows the results of our proposed model and four other methods on the NYU/RESIDE dataset. The base model performed better than the methods that utilized hand-crafted features (DCP, BCCR [40]) and the deep learning-based method (AOD-Net). Our proposed model achieved a further improvement in accuracy (nearly an increment of 2 dB in PSNR over the base model) and outperformed all other methods. Table 5 shows the results with the SOTS dataset. Again, our proposed model outperformed all other methods in terms of all evaluation metrics. The improvements over the base model were due to the changes to the cGAN structure and the activation function. Our proposed model could learn better from the training samples with the additional computation layers for feature extraction. Moreover, the training was more effective with the new activation function that could provide better gradient flow. Table 6 compares the inference time per image between our proposed model and the base model. Since our image dehazing model contained more computation layers than the base model, it demanded a longer inference time. Our model was still able to generate the dehazed image in near real-time speed. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **Score** \\ \hline DCP [8] & 17.23 & 0.803 & 1.665 \\ \hline BCCR [40] & 15.12 & 0.719 & 1.475 \\ \hline AOD-Net [14] & 11.63 & 0.624 & 1.206 \\ \hline Base model [36] & 23.18 & 0.845 & 2.004 \\ \hline Our model & 25.14 & 0.876 & 2.133 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of image dehazing on NYU/RESIDE dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **PSNR** & **SSIM** & **Score** \\ \hline DCP [8] & 18.66 & 0.873 & 1.806 \\ \hline BCCR [40] & 14.02 & 0.757 & 1.458 \\ \hline AOD-Net [14] & 18.48 & 0.833 & 1.757 \\ \hline Base model [36] & 22.24 & 0.891 & 2.003 \\ \hline Our model & 22.91 & 0.901 & 2.046 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of image dehazing on SOTS dataset. The score parameter was ranked as medium, good or excellent. A score of 1.7 was considered medium (i.e., PSNR = 20 dB, SSIM = 0.7). A score of 2.0 was considered good (i.e., PSNR = 24 dB, SSIM = 0.8). A score of 2.5 was considered excellent (i.e., PSNR = 30 dB, SSIM = 1). According to this ranking scheme, the DCP and AOD-Net were between medium to good, and the base model was good. Our proposed model, as shown from the results on NYU/RESIDE and SOTS datasets in Tables 4 and 5, Table 4 was ranked between good and excellent. Figure 8 shows some visual results (indoor and outdoor images) on the NYU/RESIDE dataset. The methods that employ hand-crafted features produced darker images with distorted colors. AOD-Net produced more natural airlight in the outdoor scene, but the image was still dark. It was unable to remove the haze in the indoor image (it also had the worst numerical results in Table 3).