Vehicle Detection Method with Sub-stretching Based on Light Contrast in Fog Road Image

In this paper, detection of a vehicle from a road image with fog is focused to detect an vehicle from a foggy image. Because of the fog in the image, a shape of an object is vague. Therefore an obstacle may occur on the vehicle detection. Thus, features from a foggy road image are surveyed through experinmlts, and a histogram is caloalated with the bright value. The stretching method is then applied with the specific weight as the centre to detect a vehicle smoothly. If the high density area, from the view point of histogram, is applied with the stretching method, the definition of the image can be increased. On this fact, this paper proposed a method to divide the histogram and to determine applicable range of the stretching method. The improved results by the proposed methods are proved with the camparison tests between the proposed and previous methods.

Computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades.Nonetheless,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these problems.This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft Kinect.It then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole detection.The article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic segmentation.We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.