# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 6 Conclusions  
We designed a novel single end-to-end network based on the cGAN framework for image dehazing. We strengthened the analytical power of the network via the adoption of convolutional blocks with progressively more layers. Moreover, the entire dehazing framework was enhanced with the adoption of a new non-linear activation function in both the generator and discriminator modules. The dehazed image was beneficial for the detection of objects in degraded road scene images. Based on the enhanced framework, we proposed two image dehazing models with two discriminators. Through thorough experimentation, we demonstrated that our proposed image dehazing models improved the visibility of the degraded images and resulted in a higher accuracy of object detection.  
Figure 15: Object detection results with RTTS—left column: original hazy images; right column: dehazed images generated using our proposed model 2.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**\# Labels**} & \multicolumn{3}{c}{**Original Hazy Images**} & \multicolumn{3}{c}{**Dehazed Image by Model 2**} \\ \cline{3-6}  & & **mAP @0.5** & **mAP @0.95** & **mAP @0.5** & **mAP @0.95** \\ \hline Pedestrian & 2447 & 0.622 & 0.337 & 0.570 & 0.303 \\ \hline Bicycle & 338 & 0.617 & 0.418 & 0.664 & 0.443 \\ \hline Car & 4430 & 0.595 & 0.413 & 0.602 & 0.422 \\ \hline Bus & 6 & 0.039 & 0.031 & 0.035 & 0.029 \\ \hline Truck & 127 & 0.066 & 0.047 & 0.083 & 0.057 \\ \hline All & 7348 & 0.388 & 0.249 & 0.391 & 0.251 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Object detection accuracy with RTTS.  
Our proposed models outperformed not only the hand-crafted and deep learning-based methods, but also the cGAN base model using various datasets.  
We will continue our research on image dehazing. In the current work, we investigated the impact of our proposed image dehazing model on object detection performed on images degraded with rain and fog effects. More experimentation will be performed to investigate the capability of our proposed model in tackling other types of image degradation. Besides object detection in 2D images, we will also explore the impact of image dehazing on other applications such as monocular 3D object detection.  
For practical applications, we will consider integrating the image dehazing as an optional module within the object detection framework. To save computational costs, there is no need to perform image dehazing when the scene visibility is good. Automatically triggering the execution of the image dehazing module is possible via computation of the similarity between the hazy input image and the dehazed output image. Significant changes in quantitative measures, e.g., SSIM, will enable the continuing function of the image dehazing module. An automatic alert of extreme conditions would be possible when the confidence score of an object detection is low.  
T.-Y.C.: methodology, investigation, software, writing--original draft preparation; K.-H.L.: methodology, investigation, software, writing--original draft preparation; K.-L.C.: conceptualization, methodology, supervision, writing--original draft preparation, writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 11202319), and City University of Hong Kong Strategic Research Grant (Project No. 7005855). The authors gratefully acknowledge the support of Lexiwave Technology (Hong Kong) Ltd.  
Not applicable.  
The authors gratefully acknowledge the support of Lexiwave Technology (Hong Kong) Ltd.  
The authors declare no conflict of interest.