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Asphalt pavement raveling identification based on machine learning
Raveling on asphalt surfaces can lead to the occurrence of diseases such as loose potholes on the road surface. Monitoring and real-time warning of the development status of pavement raveling helps formulate road maintenance plans in time and effectively curb the occurrence and development of pavement diseases. To realize the intelligent identification of asphalt pavement raveling, the LS-40 portable 3D surface analyzer was used to collect the image data of asphalt pavement raveling and normal asphalt pavement, and the recognition effects of traditional image classification and recognition models supported vector machine (SVM), random forest and convolutional neural network (CNN) were compared. The results show that the random forest model has a good accuracy of 82% for the recognition of pavement raveling images based on the traditional machine learning model on the high-precision texture data dataset, and the convolutional neural network has an accuracy of 93%, which is 11% higher than that of random forest. The results verify the efficiency of the convolutional neural network in the identification of pavement raveling diseases.
Asphalt pavement raveling identification based on machine learning
Raveling on asphalt surfaces can lead to the occurrence of diseases such as loose potholes on the road surface. Monitoring and real-time warning of the development status of pavement raveling helps formulate road maintenance plans in time and effectively curb the occurrence and development of pavement diseases. To realize the intelligent identification of asphalt pavement raveling, the LS-40 portable 3D surface analyzer was used to collect the image data of asphalt pavement raveling and normal asphalt pavement, and the recognition effects of traditional image classification and recognition models supported vector machine (SVM), random forest and convolutional neural network (CNN) were compared. The results show that the random forest model has a good accuracy of 82% for the recognition of pavement raveling images based on the traditional machine learning model on the high-precision texture data dataset, and the convolutional neural network has an accuracy of 93%, which is 11% higher than that of random forest. The results verify the efficiency of the convolutional neural network in the identification of pavement raveling diseases.
Asphalt pavement raveling identification based on machine learning
Du, Kelin (editor) / Mohd Zain, Azlan bin (editor) / Li, Zheng (author) / Peng, Yi (author) / Liu, Maoyi (author) / Jiang, Xin (author) / Yu, Xinyi (author) / Zhang, Zhengqi (author) / Wang, Bo (author) / Chen, Kunping (author)
Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024) ; 2024 ; Kuala Lumpur, Malaysia
Proc. SPIE ; 13250
2024-08-23
Conference paper
Electronic Resource
English
Engineering Index Backfile | 1959
TR-120 EFFECT OF ASPHALT CONCRETE RAVELING ON PAVEMENT ROUGHNESS
| British Library Conference Proceedings | 2002