Open AccessArticle
Deep Learning Model for A Driver Assistance System to Increase Visibility on A Foggy Road
LIST Laboratory, FSTT, UAE University, Tangier 90000, Morocco
*whom correspondence should be addressed. E-mail: allach.samir@gmail.com
Adv. Sci. Technol. Eng. Syst. J. 5(4), 314–322 (2020);
DOI: 10.25046/aj050437
Received: 22 May 2020, Accepted: 13 July 2020, Published Online: 28 July 2020
(This article belongs to the SP9 (Special Issue on Multidisciplinary Innovation in Engineering Science & Technology 2020) & Section Transportation Science & Technology (TST))
For many years, a lot of researches have been made to develop Advanced Driver Assistance Systems (ADAS) that are based on integrated systems. The main objective is to help drivers. Hence, keeping them safe under different driving conditions. Visibility for drivers remains the biggest problem faced on the road in an atmosphere of fog. In this paper, we examine a system that can be employed to substantially enhance visibility through using deep neural networks. Researches done recently- which are based on deep learning for eliminating image fog- have made clear that an end-to-end proposed system is such an effective model. However, it becomes a must to extend the idea to end-to-end real-time video deshazing. In this paper, we introduce a model of image dehazing. It is based on Convolutional Neural Networks (CNN) as a basis for developing the video dehazing model. As in addition, we concatenate our model with the faster RCNN for detecting objects on the road in real time. The experimental results on our image datasets shows the performance of our model with regard to Peak Signal to Noise Ratio (PSNR=19.823) and Structural Similarity (SSIM =0.8501). On the dataset of the synthesized videos, our model achieved a performance of PSNR = 21.4032 and SSIM = 0.9354. Moreover, with the concatenation of our dehazing model with Faster R-CNN (regions with convolutional neural networks), our proposed system displays desirable visual quality and a remarkable progress of the object detection achievement on blurred images with mean Average Precision (mAP) equal to 0.933 during the day and 0.804 during the night.
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