Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis

Abstract

Several data processing techniques (DPTs) have been implemented for evaluating pavement surface texture to partially replace onsite inspections by humans. However, the extensively varying real‐world situations have resulted in challenges in the widespread adoption of DPTs. To overcome these challenges, we propose the use of a convolutional neural network (CNN) to calculate the mean texture depth (MTD) without computing the surface texture feature statistics. Because a CNN is capable of automatically learning data features, the proposed method does not require the conjugation of DPTs for extracting features. The proposed CNN was trained and tested using 8,000 and 1,000 3D scan data samples, respectively, and achieved an average error of 0.0024 cm. The stability of the CNN was analyzed based on various test results. Comparative studies were conducted to verify the superiority of the CNN over conventional MTD algorithms. The results demonstrated that the CNN‐based method is significantly more stable in various real‐world situations. Additionally, the CNN‐based method achieved higher accuracy of automatic feature extraction than traditional MTD methods. Finally, the CNN‐based method was applied to evaluate the surface texture statistics of four highways in Shanxi, China, which were different from the training and testing samples; the results establish the transferability of this method to different highways.

Uncertainty‐assisted deep vision structural health monitoring

“…There have been efforts to apply these algorithms in material properties prediction (Rafiei, Khushefati, Demirboga, & Adeli, 2017), asphalt surface analysis (Tong, Gao, Sha, Hu, & Li, 2018), recovering lost sensor data (Oh, Glisic, Kim, & Park, 2019), early earthquake warning systems (Rafiei & Adeli, 2017a), and construction management (Rafiei & Adeli, 2016, 2018a). More specifically, SHM research has significantly benefited from utilizing deep learning in processing information to assess structural conditions.…”

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

“…Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018;K. Maeda, Takahashi, Ogawa, & Haseyama, 2019;Tong, Gao, Sha, Hu, & Li, 2018; and vehicle noise measurement (Ambrosini, Gabrielli, Vesperini, Squartini, & Cattani, 2018) have been studied. However, the vehicle vibration-based road roughness estimation using deep learning-based approaches has not been proposed yet.…”

Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach

“…Furthermore, to better demonstrate the prediction capability of the newly constructed MO-SVM-PCD employed for detecting metal pipe corrosion, its performance has been compared to that of the least squares support vector machine (LSSVM) [51], classification tree (CTree) [52], backpropagation artificial neural network (BPANN) [53], and convolutional neural network (CNN) [54]. The reason for the selection of these benchmark models is that they have been confirmed to be capable methods for pattern classification by previous studies [5, 40, 55–57].…”