The Experts below are selected from a list of 6816 Experts worldwide ranked by ideXlab platform
Rui Fan - One of the best experts on this subject based on the ideXlab platform.
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rethinking road surface 3 d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.
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rethinking road surface 3d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved.
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road damage detection based on unsupervised Disparity Map segmentation
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Rui Fan, Ming LiuAbstract:This article presents a novel road damage detection algorithm based on unsupervised Disparity Map segmentation. Firstly, a Disparity Map is transformed by minimizing an energy function with respect to stereo rig roll angle and road Disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed Disparity Map is then segmented using Otus’s thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The experimental results illustrate that our proposed algorithm performs both accurately and efficiently. The pixel-level road damage detection accuracy is approximately 97.56%. The source code is publicly available at: https://github.com/ruirangerfan/unsupervised_Disparity_Map_segmentation.git .
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multiple lane detection algorithm based on optimised dense Disparity Map estimation
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Jianhao Jiao, Ming Liu, Usman Maqbool M Bhutta, Mohammud Junaid Bocus, Lujia Wang, Rui FanAbstract: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%.
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multiple lane detection algorithm based on optimised dense Disparity Map estimation
International Conference on Imaging Systems and Techniques, 2018Co-Authors: Jianhao Jiao, Ming Liu, Usman Maqbool M Bhutta, Mohammud Junaid Bocus, Lujia Wang, Rui FanAbstract: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 $t_{n+1}(n \ge 0)$. 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 ${99{\%}}$.
Ming Liu - One of the best experts on this subject based on the ideXlab platform.
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rethinking road surface 3 d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.
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rethinking road surface 3d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved.
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road damage detection based on unsupervised Disparity Map segmentation
IEEE Transactions on Intelligent Transportation Systems, 2020Co-Authors: Rui Fan, Ming LiuAbstract:This article presents a novel road damage detection algorithm based on unsupervised Disparity Map segmentation. Firstly, a Disparity Map is transformed by minimizing an energy function with respect to stereo rig roll angle and road Disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed Disparity Map is then segmented using Otus’s thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The experimental results illustrate that our proposed algorithm performs both accurately and efficiently. The pixel-level road damage detection accuracy is approximately 97.56%. The source code is publicly available at: https://github.com/ruirangerfan/unsupervised_Disparity_Map_segmentation.git .
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multiple lane detection algorithm based on optimised dense Disparity Map estimation
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Jianhao Jiao, Ming Liu, Usman Maqbool M Bhutta, Mohammud Junaid Bocus, Lujia Wang, Rui FanAbstract: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%.
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multiple lane detection algorithm based on optimised dense Disparity Map estimation
International Conference on Imaging Systems and Techniques, 2018Co-Authors: Jianhao Jiao, Ming Liu, Usman Maqbool M Bhutta, Mohammud Junaid Bocus, Lujia Wang, Rui FanAbstract: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 $t_{n+1}(n \ge 0)$. 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 ${99{\%}}$.
Yassine Ruichek - One of the best experts on this subject based on the ideXlab platform.
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parallel structured mesh generation with Disparity Maps by gpu implementation
IEEE Transactions on Visualization and Computer Graphics, 2015Co-Authors: Hongjian Wang, Naiyu Zhang, Julien Moreau, Jean-charles Créput, Yassine RuichekAbstract:The goal of structured mesh is to generate a compressed representation of the 3D surface, where near objects are provided with more details than objects far from the camera, according to the Disparity Map. The solution is based on the Kohonens Self-Organizing Map algorithm for the benefits of its ability to generate a topological Map according to a probability distribution and its potential to be a natural massive parallel algorithm. The Disparity Map, which stands for a density distribution that reflects the proximity of objects to the camera, is partitioned into an appropriate number of cell units, in such a way that each cell is associated to a processing unit and responsible of a certain area of the plane. The advantage of the proposed model is that it is decentralized and based on data decomposition. The required processing units and memory are with linearly increasing relationship to the problem size. Experimental results show that our GPU implementation is able to provide near real-time performance with small size Disparity Maps and the running time increases in a linear way with a very weak increasing coefficient. The proposed method is suitable to deal with large scale problems in a massively parallel way.
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parallel structured mesh generation with Disparity Maps by gpu implementation
IEEE Transactions on Visualization and Computer Graphics, 2015Co-Authors: Hongjian Wang, Naiyu Zhang, Julien Moreau, Jean-charles Créput, Yassine RuichekAbstract:The goal of structured mesh is to generate a compressed representation of the 3D surface, where near objects are provided with more details than objects far from the camera, according to the Disparity Map. The solution is based on the Kohonens Self-Organizing Map algorithm for the benefits of its ability to generate a topological Map according to a probability distribution and its potential to be a natural massive parallel algorithm. The Disparity Map, which stands for a density distribution that reflects the proximity of objects to the camera, is partitioned into an appropriate number of cell units, in such a way that each cell is associated to a processing unit and responsible of a certain area of the plane. The advantage of the proposed model is that it is decentralized and based on data decomposition. The required processing units and memory are with linearly increasing relationship to the problem size. Experimental results show that our GPU implementation is able to provide near real-time performance with small size Disparity Maps and the running time increases in a linear way with a very weak increasing coefficient. The proposed method is suitable to deal with large scale problems in a massively parallel way.
Ioannis Pitas - One of the best experts on this subject based on the ideXlab platform.
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rethinking road surface 3 d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.
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rethinking road surface 3d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved.
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Pothole Detection Based on Disparity Transformation and Road Surface Modeling
IEEE Transactions on Image Processing, 2020Co-Authors: Umar Ozgunalp, Brett Hosking, Ioannis PitasAbstract:Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense Disparity Map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater Disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu's thresholding method is then used to extract potential undamaged road areas from the transformed Disparity Map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve Disparity Map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled Disparity Maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%.
Umar Ozgunalp - One of the best experts on this subject based on the ideXlab platform.
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rethinking road surface 3 d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
IEEE Transactions on Systems Man and Cybernetics, 2021Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.
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rethinking road surface 3d reconstruction and pothole detection from perspective transformation to Disparity Map segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Rui Fan, Umar Ozgunalp, Ming Liu, Yuan Wang, Ioannis PitasAbstract:Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road Disparity Map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A Disparity Map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved.
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Pothole Detection Based on Disparity Transformation and Road Surface Modeling
IEEE Transactions on Image Processing, 2020Co-Authors: Umar Ozgunalp, Brett Hosking, Ioannis PitasAbstract:Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense Disparity Map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater Disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu's thresholding method is then used to extract potential undamaged road areas from the transformed Disparity Map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve Disparity Map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled Disparity Maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%.
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block matching Disparity Map estimation using controlled search range
Computer Science and Electronic Engineering Conference, 2015Co-Authors: Umar Ozgunalp, Z Zhang, G Koc, Naim DahnounAbstract:In this paper, Disparity Map estimation algorithm targeting for advanced driver assistance systems (ADAS) is described. A Disparity Map estimation algorithm for ADAS needs to be both accurate and to be able to work in real time. These algorithms can be classified into two categories: global estimation and local estimation. While global estimation algorithms tend to output a less noisy Disparity Map, most of them cannot operate in real time. In this paper, disparities are estimated locally by block matching and information acquired by calculating a Disparity value for a pixel is transferred to its neighborhood by restricting the search range. Thus, in this way, while great efficiency is achieved during the block matching process, the effect of outliers is also minimized. The proposed algorithm suggests a new Disparity calculations order which makes it possible for a pixel to get support from three different neighborhood directions (i.e. left, right and bottom). Using Horizontal support (from both directions) along with vertical support further reduces the noise compared to using support from only one direction (bottom). Thus, an accurate Disparity Map can be calculated while low computational complexity is maintained. Experimental results are quantified using KITTI data-sets and the percentage of erroneous pixels in non-occluded areas (Out-Noc) is calculated as 8.86%, while run-time is estimated as 0.84 seconds on an i7-870 CPU using single thread C implementation.