Pothole Detection and Classification Using 3D Technology and Watershed Method

Tsai, Yichang; Chatterjee, Anirban

doi:10.1061/(asce)cp.1943-5487.0000726

Cited by 84 publications

(40 citation statements)

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“…The desirable roll angle θ can, therefore, be determined by finding the highest value between g(θ 1 ) and g(θ 2 ). a can then be obtained by substituting θ into (7). Each road disparity can now be represented using:…”

Section: Preliminariesmentioning

confidence: 99%

“…On the other hand, 2D image analysis-based road damage detection methods can be grouped into two categories: computer vision-based [6], [7], [12]- [15] and machine learningbased [16]- [19]. The former typically pre-processes a 2D image, i.e., an RGB/gray-scale image or a depth/disparity map, using some image processing techniques, e.g., various image filters, to reduce image noise and enhance road damage outline [12], [13].…”

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confidence: 99%

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Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Fan

Liu

2020

IEEE Trans. Intell. Transport. Syst.

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This paper 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%.Index Terms-road damage detection, disparity map segmentation, stereo rig roll angle, road disparity projection model, numerical solution.

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Section: Preliminariesmentioning

confidence: 99%

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confidence: 99%

Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Fan

Liu

2020

IEEE Trans. Intell. Transport. Syst.

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“…Over the past decade, various technologies, such as active and passive sensing, have been utilized to acquire road data and aid personnel in detecting road potholes [5]. For example, Tsai and Chatterjee [6] mounted two laser scanners on a digital inspection vehicle (DIV) to collect 3D road surface data. These data were then processed using either semi or fully automatic methods for pothole detection.…”

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confidence: 99%

“…These data were then processed using either semi or fully automatic methods for pothole detection. Such systems ensure personnel safety, but also reduce the need for manual intervention [6]. Furthermore, by comparing the road data collected over different periods, the traffic flow can be evaluated and the future road condition can be predicted [2].…”

mentioning

confidence: 99%

“…However, both color and gray-scale image segmentation techniques are severely affected by various factors, most notably by poor illumination conditions [11]. Therefore, some authors proposed to perform segmentation on the depth maps, which has shown to achieve better performance when separating damaged and undamaged road areas [6], [11]. Furthermore, the shapes of actual potholes are always irregular, making the geometric and textural assumptions occasionally unreliable.…”

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Pothole Detection Based on Disparity Transformation and Road Surface Modeling

Fan

Özgünalp

Hosking

et al. 2020

IEEE Trans. on Image Process.

183

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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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Pothole Detection Using Deep Convolutional Neural Network

Prakash

Velampalli

2021

Advances in Intelligent Systems and Computing

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Pothole Detection and Classification Using 3D Technology and Watershed Method

Cited by 84 publications

References 20 publications

Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Pothole Detection Based on Disparity Transformation and Road Surface Modeling

Pothole Detection Using Deep Convolutional Neural Network

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