Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis

2018 ◽  
Vol 33 (12) ◽  
pp. 1056-1072 ◽  
Author(s):  
Zheng Tong ◽  
Jie Gao ◽  
Aimin Sha ◽  
Liqun Hu ◽  
Shuai Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Texture Analysis ◽  
Surface Texture ◽  
Asphalt Pavement ◽  
Pavement Surface ◽  
Surface Texture Analysis

Automated Asphalt Pavement Raveling Detection and Classification using Convolutional Neural Network and Macrotexture Analysis

2021 ◽  
pp. 036119812110054
Author(s):  
Yung-An Hsieh ◽  
Yichang (James) Tsai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asphalt Pavement ◽  
Training Data ◽  
Data Driven ◽  
Pavement Surface ◽  
Surface Data ◽  
Proposed Model ◽  
Combining Data ◽  
Pavement Distresses

Raveling is one of the most common asphalt pavement distresses. The survey of its condition is required for transportation agencies to ensure roadway safety and appropriately apply preservation and rehabilitation treatments. However, the traditional raveling condition survey, including the determination of the raveling severity, is typically manually conducted by in-field visual inspection methods that are time consuming, labor intensive, and error prone. Although automated raveling detection and severity classification models have been developed, these existing models have shortcomings. Therefore, there is an urgent need to develop a more accurate and reliable model to automatically detect and classify raveling. This study proposes the first convolutional neural network (CNN)-based model for automated raveling detection and classification. Compared with general CNNs, the proposed model combines the data-driven features learned from training data and macrotexture features of 3D pavement surface data to achieve better performance. The proposed model was evaluated and compared with existing machine learning models using real-world 3D pavement surface data collected from the state of Georgia, U.S. By combining data-driven features with macrotexture features, the proposed model achieved the highest accuracy of 90.8% on raveling classification. The proposed model also achieved classification precision and recall higher than 85% for all raveling severity levels, which is more accurate and robust than existing models. It is concluded that, with multi-type features extraction and proper model design, the proposed model can provide more accurate and reliable predictions for raveling detection and classification.

AN AUTOREGRESSIVE APPROACH TO SURFACE TEXTURE ANALYSIS

1994 ◽  
Vol 08 (04) ◽  
pp. 845-857 ◽  
Author(s):  
DINESH P. MITAL ◽  
GOH WEE LENG
Keyword(s):  
Aspect Ratio ◽  
Random Field ◽  
Texture Analysis ◽  
Surface Texture ◽  
Field Model ◽  
Invariant Property ◽  
Circular Area ◽  
Pixel Image ◽  
Rotational Invariant ◽  
Surface Texture Analysis

The use of autoregressive models in textual analysis holds great potential. Coupling the technique to a circular neighbourhood set imparts a rotational invariant property to it. This was demonstrated by Kashyap and Khotanzad in their model called the Circular Symmetric Autogressive (CSAR) Random Field model. The short-coming in this very ingenious proposal is that it is set in a background of square pixels and the rotational invariant property of the model fails in cases when the aspect ratio of the pixels are not at unity. This paper proposes a major modification to the CSAR to render the model rotational invariant under all configurations of pixel implementation. It is based on the area segments covered by a circle set in a 3×3 neighbourhood. We call it the Circular Area Autoregressive (CAAR) model. The results obtained from the CAAR showed much better consistency over that of the CSAR when a non-square pixel image was used.

Deep Learning for Asphalt Pavement Cracking Recognition Using Convolutional Neural Network

2017 ◽  
Author(s):  
Kelvin C. P. Wang ◽  
Allen Zhang ◽  
Joshua Qiang Li ◽  
Yue Fei ◽  
Cheng Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Asphalt Pavement

Convolutional neural network for pothole detection in asphalt pavement

2019 ◽  
Vol 22 (1) ◽  
pp. 42-58 ◽  
Author(s):  
Wanli Ye ◽  
Wei Jiang ◽  
Zheng Tong ◽  
Dongdong Yuan ◽  
Jingjing Xiao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asphalt Pavement ◽  
Pothole Detection
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