13 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
Real-Time Stereo Vision for Road Surface 3-D Reconstruction
Stereo vision techniques have been widely used in civil engineering to
acquire 3-D road data. The two important factors of stereo vision are accuracy
and speed. However, it is very challenging to achieve both of them
simultaneously and therefore the main aim of developing a stereo vision system
is to improve the trade-off between these two factors. In this paper, we
present a real-time stereo vision system used for road surface 3-D
reconstruction. The proposed system is developed from our previously published
3-D reconstruction algorithm where the perspective view of the target image is
first transformed into the reference view, which not only increases the
disparity accuracy but also improves the processing speed. Then, the
correlation cost between each pair of blocks is computed and stored in two 3-D
cost volumes. To adaptively aggregate the matching costs from neighbourhood
systems, bilateral filtering is performed on the cost volumes. This greatly
reduces the ambiguities during stereo matching and further improves the
precision of the estimated disparities. Finally, the subpixel resolution is
achieved by conducting a parabola interpolation and the subpixel disparity map
is used to reconstruct the 3-D road surface. The proposed algorithm is
implemented on an NVIDIA GTX 1080 GPU for the real-time purpose. The
experimental results illustrate that the reconstruction accuracy is around 3
mm.Comment: 6 pages, 4 figures, IEEE International Conference on Imaging System
and Techniques (IST) 2018. arXiv admin note: substantial text overlap with
arXiv:1807.0204
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
Collision-free space detection is a critical component of autonomous vehicle
perception. The state-of-the-art algorithms are typically based on supervised
learning. The performance of such approaches is always dependent on the quality
and amount of labeled training data. Additionally, it remains an open challenge
to train deep convolutional neural networks (DCNNs) using only a small quantity
of training samples. Therefore, this paper mainly explores an effective
training data augmentation approach that can be employed to improve the overall
DCNN performance, when additional images captured from different views are
available. Due to the fact that the pixels of the collision-free space
(generally regarded as a planar surface) between two images captured from
different views can be associated by a homography matrix, the scenario of the
target image can be transformed into the reference view. This provides a simple
but effective way of generating training data from additional multi-view
images. Extensive experimental results, conducted with six state-of-the-art
semantic segmentation DCNNs on three datasets, demonstrate the effectiveness of
our proposed training data augmentation algorithm for enhancing collision-free
space detection performance. When validated on the KITTI road benchmark, our
approach provides the best results for stereo vision-based collision-free space
detection.Comment: accepted to IEEE/ASME Transactions on Mechatronic
Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
Lane detection is very important for self-driving vehicles. In recent years,
computer stereo vision has been prevalently used to enhance the accuracy of the
lane detection systems. This paper mainly presents a multiple lane detection
algorithm developed based on optimised dense disparity map estimation, where
the disparity information obtained at time t_{n} is utilised to optimise the
process of disparity estimation at time t_{n+1}. This is achieved by estimating
the road model at time t_{n} and then controlling the search range for the
disparity estimation at time t_{n+1}. The lanes are then detected using our
previously published algorithm, where the vanishing point information is used
to model the lanes. The experimental results illustrate that the runtime of the
disparity estimation is reduced by around 37% and the accuracy of the lane
detection is about 99%.Comment: 5 pages, 7 figures, IEEE International Conference on Imaging Systems
and Techniques (IST) 201