# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 5 Experiments and Results
### Object Detection Result  
To investigate the significance of image dehazing on object detection, we compared the accuracy of target detection using two versions of our cGAN model with the base model. We created five datasets of road scene images--clear images, rain images, fog images, dehazed rain images and dehazed fog images. Table 9 shows the object detection accuracy mAP on clear images, rain images and fog images. The rain and fog images were generated with the weather effects synthesized and overlayed on the clear images. No image dehazing was performed on the rain and fog images. It was clear that using degraded images would lead to lower object detection accuracy in all object classes. The overall mAP on rain images and fog images dropped by 0.012 and 0.024, respectively, as compared with the corresponding result with clear images. In some categories, the reduction in accuracy was more substantial. For instance, in the class of pedestrian, the mAP on rain images and fog images reduced by 0.042 and 0.052, respectively, as compared with the corresponding result for clear images. Table 10 compares two versions of our cGAN model with the base model using dehazed rain images. The utilization of the cGAN framework for image dehazing could significantly improve the object detection accuracy. Our proposed models achieved a further gain in accuracy in some object classes (e.g., pedestrian, bicycle, car) and also the highest overall accuracy. Table 11 compares two versions of our cGAN model with the base model, FFA-Net [18], and MSBDN [20] using dehazed fog images. Although fog is a more serious defect than rain, the object detection accuracy on the dehazed fog dataset was close to that obtained with the dehazed rain dataset. This demonstrates the effectiveness of the cGAN framework in tackling different types of degradation. Our proposed models achieved better performance than the base model in some object classes (e.g., pedestrian, bicycle, bus), and also the highest overall accuracy. Our proposed model 2 also achieved better performance than FFA-Net in the object classes of car and bus. The confidence score was a numeric result (between 0 to 1) produced by the object detector. It represents the likelihood of the recognized class of object. Table 12 compares the average confidence scores of degraded images and dehazed images. cGAN improved the confidence score of the dehazed rain image and fog image by 0.013 and 0.049, respectively, over the degraded images. This further illustrated the benefit of dehazed images over degraded images in object detection, in particular with regard to foggy images. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Base Model [36]**} & \multicolumn{2}{c}{**Our Model 1 (\(G\) + D1)**} & \multicolumn{2}{c}{**Our Model 2 (\(G\) + D2)**} \\ \cline{2-7}  & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** \\ \hline Pedestrian & 0.766 & 0.444 & 0.821 & 0.459 & 0.777 & 0.462 \\ \hline Bicycle & 0.865 & 0.644 & 0.868 & 0.658 & 0.868 & 0.631 \\ \hline Car & 0.957 & 0.680 & 0.966 & 0.674 & 0.956 & 0.678 \\ \hline Bus & 0.969 & 0.849 & 0.966 & 0.838 & 0.974 & 0.856 \\ \hline Truck & 0.958 & 0.796 & 0.929 & 0.796 & 0.956 & 0.802 \\ \hline All & 0.903 & 0.683 & 0.910 & 0.685 & 0.906 & 0.686 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Object detection accuracy with dehazed rain images.