Real-Time Subpixel Fast Bilateral Stereo

Fan, Rui; Liu, Yanan; Bocus, Mohammud Z.; Wang, Lujia; Liu, Ming

doi:10.1109/icinfa.2018.8812594

Cited by 16 publications

(12 citation statements)

References 29 publications

(41 reference statements)

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“…Since the images have already been classified using our proposed deep neural network, only the positive images are considered for processing in this subsection. Before performing image segmentation, we first utilize a bilateral filter [40], [41] to smooth the input images. Bilateral filter outperforms other image filters in terms of edge preservation [40].…”

Section: B Image Segmentationmentioning

confidence: 99%

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Fan

Bocus

Zhu³

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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136

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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. 1 R. Fan, J. Jiao and M. Liu are with the Robotics and Multi-Perception Laboratory in Robotics Institute at

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Section: B Image Segmentationmentioning

confidence: 99%

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Fan

Bocus

Zhu³

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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136

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“…These methods are also known as fast bilateral stereo, where both intensity difference and spatial distance provide a Gaussian weighting function to adaptively constrain the cost aggregation from the neighbours. A general representation of the cost aggregation in FBS is as follows [35]:…”

Section: B Disparity Estimationmentioning

confidence: 99%

Real-Time Stereo Vision for Road Surface 3-D Reconstruction

Fan

Liu

Yang

et al. 2018

2018 IEEE International Conference on Imaging Systems and Techniques (IST)

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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.

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“…In our previous paper [21], a likelihood function V (p e ) = ∇(p e ) · cos(θ pe − θ pvp ) is formed for each edge point p e and the plus-minus peak pairs are selected for lane visualisation, where θ pe is the angle between the u-axis and the orientation of the edge point p e , and θ pvp is the angle between the u-axis and the radial from an edge pixel p e to p vp (v e ). More details are provided in [21], [24]. The lanes can thus be visualised using f (v), g(v) and V (p e ).…”

Section: Lane Detectionmentioning

confidence: 99%

Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation

Jiao

et al. 2018

2018 IEEE International Conference on Imaging Systems and Techniques (IST)

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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 tn is utilised to optimise the process of disparity estimation at time tn+1 (n ≥ 0). This is achieved by estimating the road model at time tn and then controlling the search range for the disparity estimation at time tn+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%.

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Real-Time Subpixel Fast Bilateral Stereo

Cited by 16 publications

References 29 publications

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Real-Time Stereo Vision for Road Surface 3-D Reconstruction

Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation

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