A novel approach for pavement texture characterisation using 2D-wavelet decomposition

Du, Yuchuan; Weng, Zihang; Feng, Lei; Ablat, Gulnigar; Wu, Difei; Liu, Chenglong

doi:10.1080/10298436.2020.1825712

Cited by 57 publications

(8 citation statements)

References 29 publications

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“…In case of insufficient observed data, Maeda et al applied a generative adversarial network (GAN) to generate distress images that could be used as new training data to improve detection accuracy (Maeda et al, 2021). Besides, a new pavement crack detection method was proposed to improve the detection accuracy by combining 2D grayscale images and 3D laser scanning data (Du et al, 2022;Huang et al, 2014;Weng et al, 2022). Some other advanced models were also proposed, such as functional brain network (Hua et al, 2019), parameter sharing-based deep network (Reyes & Ventura, 2019), deep support vector neural networks (Diaz-Vico et al, 2020), and so on.…”

Section: Pavement Distress Detection and Feature Extraction Methodsmentioning

confidence: 99%

Spatiotemporal matching method for tracking pavement distress using high‐frequency detection data

Pan

Liu

et al. 2022

Computer aided Civil Eng

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Various algorithms based on deep learning have achieved promising results in pavement distress detection. However, the detected distresses are not tracked throughout the life cycle. In long‐term application scenarios, pavement distresses may take on different forms due to image acquisition mode, distress development, and environmental change, which make tracking distresses a tough question. We present in this study a spatiotemporal matching method based on high‐frequency real pavement distress datasets. Pavement distresses of fixed routes were collected 30 times over 5 months, and distresses with spatiotemporal information were obtained at time series. We apply image rectification, stitching and distress class, and bounding box generation algorithms for pre‐processing to align the collected images to the same‐detail level and angle. A four‐step spatiotemporal matching module is designed, including global positioning system (GPS) filtering, class filtering, relative position filtering, and distress feature filtering. The results reveal that the comprehensive rank‐3 hit rate of the matching method reaches 88.73%, and the method is robust to environmental factors, which helps show performance decay of distresses and the effect of maintenance operations. It is concluded that the spatiotemporal matching method is convenient to operate, and it lays the foundation for an agency to track distress evolution and make timely treatment of distresses in the life cycle.

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Section: Pavement Distress Detection and Feature Extraction Methodsmentioning

confidence: 99%

Spatiotemporal matching method for tracking pavement distress using high‐frequency detection data

Pan

Liu

et al. 2022

Computer aided Civil Eng

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“…Recently, researchers came up with several novel indexes to better characterize pavement texture, including the amplitude, distribution, shape, and hybrid parameters, to build the correlation model with the ground truth friction (Čelko et al., 2016; Du et al., 2021; Hu et al., 2016; Zuniga‐Garcia & Prozzi, 2019). Plentiful signal processing techniques, such as the Hilbert–Huang transform (Kane et al., 2015), fractal analysis (Miao et al., 2014), power spectral analysis (Hartikainen et al., 2014), wavelet analysis (Du et al., 2022; Zelelew et al., 2013), and photo‐simulated images of surface height maps (Mahboob Kanafi et al., 2015), are applied for pavement friction estimation as well. However, the relationship between texture indexes and pavement friction is implicit and hard to quantize.…”

Section: Introductionmentioning

confidence: 99%

Effective pavement skid resistance measurement using multi‐scale textures and deep fusion network

Liu

Weng

et al. 2022

Computer aided Civil Eng

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Pavement skid resistance measurement is a fundamental component of roadway management and maintenance. Most traditional approaches rely on manual operations or heavy devices, which lead to a labor-intensive, inefficient, and vulnerable testing environment. Precise laser scanning technology lays a solid foundation for effective and continuous pavement friction measurement. This paper proposed an automated pavement friction estimation model using 3D point cloud data and a deep neural network. The fine-grained texture data of over 800 pavement sections with various anti-skidding abilities were collected. The impact of the multi-scale textures on pavement friction was separated and analyzed via two-dimensional wavelet decomposition. A multi-input fusion network with deep aggregation modules was designed to fuse the features of sub-images generated by wavelet decomposition. The results show that the average prediction error is 0.0935, outperforming most state-of-the-art models. The impact of different texture scales on friction estimation is then revealed. The proposed method provides a new tool for effective and large-scale pavement friction evaluation.

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“…Compared with the conventional laser scanning method, the LiDAR system can capture the laser point data of roads with a larger coverage (over 40 m-wide, which can cover all the lanes) and higher speed (>60 km/h). The captured laser point data of road contains rich information about road surface, including the geometrical features of interest (e.g., cracks and bumps), the pavement roughness, and even the skid resistance [16][17][18]. Although this technique is slightly less precise than other methods, it provides a rapid, large-scale solution for water layer estimation on roads.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation

Yang

Tian

Fang

et al. 2022

IJERPH

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Hydroplaning risk evaluation plays a pivotal role in highway safety management. It is also an important component in the intelligent transportation system (ITS) ensuring human driving safety. Water-film is the widely accepted vital factor resulting in hydroplaning and thus continuously gained researchers’ attention in recent years. This paper provides a new framework to evaluate the hydroplaning potential based on emerging 3D laser scanning technology and water-film thickness estimation. The 3D information of the road surface was captured using a vehicle-mounted Light Detection and Ranging (LiDAR) system and then processed by a wavelet-based filter to remove the redundant information (surrounding environment: trees, buildings, and vehicles). Then, the water film thickness on the given road surface was estimated based on a proposed numerical algorithm developed by the two-dimensional depth-averaged Shallow Water Equations (2DDA-SWE). The effect of the road surface geometry was also investigated based on several field test data in Shanghai, China, in January 2021. The results indicated that the water-film is more likely to appear on the rutting tracks and the pavement with local unevenness. Based on the estimated water-film, the hydroplaning speeds were then estimated to represent the hydroplaning risk of asphalt pavement in rainy weather. The proposed method provides new insights into the water-film estimation, which can help drivers make effective decisions to maintain safe driving.

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A novel approach for pavement texture characterisation using 2D-wavelet decomposition

Cited by 57 publications

References 29 publications

Spatiotemporal matching method for tracking pavement distress using high‐frequency detection data

Spatiotemporal matching method for tracking pavement distress using high‐frequency detection data

Effective pavement skid resistance measurement using multi‐scale textures and deep fusion network

Evaluation of Highway Hydroplaning Risk Based on 3D Laser Scanning and Water-Film Thickness Estimation

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