146 research outputs found
Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters
Vehicle manufacturers are racing to create autonomous navigation and steering
control algorithms for their vehicles. These software are made to handle
various real-life scenarios such as obstacle avoidance and lane maneuvering.
There is some ongoing research to incorporate pothole avoidance into these
autonomous systems. However, there is very little research on the effect of
hitting a pothole on the autonomous navigation software that uses cameras to
make driving decisions. Perturbations in the camera angle when hitting a
pothole can cause errors in the predicted steering angle. In this paper, we
present a new model to compensate for such angle perturbations and reduce any
errors in steering control prediction algorithms. We evaluate our model on
perturbations of publicly available datasets and show our model can reduce the
errors in the estimated steering angle from perturbed images to 2.3%, making
autonomous steering control robust against the dash cam image angle
perturbations induced when one wheel of a car goes over a pothole.Comment: 7 pages, 6 figure
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
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
UDTIRI: An Open-Source Road Pothole Detection Benchmark Suite
It is seen that there is enormous potential to leverage powerful deep
learning methods in the emerging field of urban digital twins. It is
particularly in the area of intelligent road inspection where there is
currently limited research and data available. To facilitate progress in this
field, we have developed a well-labeled road pothole dataset named Urban
Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this
dataset will enable the use of powerful deep learning methods in urban road
inspection, providing algorithms with a more comprehensive understanding of the
scene and maximizing their potential. Our dataset comprises 1000 images of
potholes, captured in various scenarios with different lighting and humidity
conditions. Our intention is to employ this dataset for object detection,
semantic segmentation, and instance segmentation tasks. Our team has devoted
significant effort to conducting a detailed statistical analysis, and
benchmarking a selection of representative algorithms from recent years. We
also provide a multi-task platform for researchers to fully exploit the
performance of various algorithms with the support of UDTIRI dataset.Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage:
https://www.kaggle.com/datasets/jiahangli617/udtir
Road surface 3D reconstruction based on dense subpixel disparity map estimation
Various 3D reconstruction methods have enabled civil engineers to detect
damage on a road surface. To achieve the millimetre accuracy required for road
condition assessment, a disparity map with subpixel resolution needs to be
used. However, none of the existing stereo matching algorithms are specially
suitable for the reconstruction of the road surface. Hence in this paper, we
propose a novel dense subpixel disparity estimation algorithm with high
computational efficiency and robustness. This is achieved by first transforming
the perspective view of the target frame into the reference view, which not
only increases the accuracy of the block matching for the road surface but also
improves the processing speed. The disparities are then estimated iteratively
using our previously published algorithm where the search range is propagated
from three estimated neighbouring disparities. Since the search range is
obtained from the previous iteration, errors may occur when the propagated
search range is not sufficient. Therefore, a correlation maxima verification is
performed to rectify this issue, and the subpixel resolution is achieved by
conducting a parabola interpolation enhancement. Furthermore, a novel disparity
global refinement approach developed from the Markov Random Fields and Fast
Bilateral Stereo is introduced to further improve the accuracy of the estimated
disparity map, where disparities are updated iteratively by minimising the
energy function that is related to their interpolated correlation polynomials.
The algorithm is implemented in C language with a near real-time performance.
The experimental results illustrate that the absolute error of the
reconstruction varies from 0.1 mm to 3 mm.Comment: 11 pages, 16 figures, IEEE Transactions on Image Processin
Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey
Ensuring traffic safety is crucial, which necessitates the detection and
prevention of road surface defects. As a result, there has been a growing
interest in the literature on the subject, leading to the development of
various road surface defect detection methods. The methods for detecting road
defects can be categorised in various ways depending on the input data types or
training methodologies. The predominant approach involves image-based methods,
which analyse pixel intensities and surface textures to identify defects.
Despite their popularity, image-based methods share the distinct limitation of
vulnerability to weather and lighting changes. To address this issue,
researchers have explored the use of additional sensors, such as laser scanners
or LiDARs, providing explicit depth information to enable the detection of
defects in terms of scale and volume. However, the exploration of data beyond
images has not been sufficiently investigated. In this survey paper, we provide
a comprehensive review of road surface defect detection studies, categorising
them based on input data types and methodologies used. Additionally, we review
recently proposed non-image-based methods and discuss several challenges and
open problems associated with these techniques.Comment: Survey paper
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