26 research outputs found
A Novel Disparity Transformation Algorithm for Road Segmentation
The disparity information provided by stereo cameras has enabled advanced
driver assistance systems to estimate road area more accurately and
effectively. In this paper, a novel disparity transformation algorithm is
proposed to extract road areas from dense disparity maps by making the
disparity value of the road pixels become similar. The transformation is
achieved using two parameters: roll angle and fitted disparity value with
respect to each row. To achieve a better processing efficiency, golden section
search and dynamic programming are utilised to estimate the roll angle and the
fitted disparity value, respectively. By performing a rotation around the
estimated roll angle, the disparity distribution of each row becomes very
compact. This further improves the accuracy of the road model estimation, as
demonstrated by the various experimental results in this paper. Finally, the
Otsu's thresholding method is applied to the transformed disparity map and the
roads can be accurately segmented at pixel level.Comment: 16 pages, 8 figure
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
Crack is one of the most common road distresses which may pose road safety
hazards. Generally, crack detection is performed by either certified inspectors
or structural engineers. This task is, however, time-consuming, subjective and
labor-intensive. In this paper, we propose a novel road crack detection
algorithm based on deep learning and adaptive image segmentation. Firstly, a
deep convolutional neural network is trained to determine whether an image
contains cracks or not. The images containing cracks are then smoothed using
bilateral filtering, which greatly minimizes the number of noisy pixels.
Finally, we utilize an adaptive thresholding method to extract the cracks from
road surface. The experimental results illustrate that our network can classify
images with an accuracy of 99.92%, and the cracks can be successfully extracted
from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu