1. Asphalt pavement crack detection based on multi-scale full convolutional network
- Author
-
Wanting Yang, Jinxiao Pan, Yangxu Wu, and Ping Chen
- Subjects
Statistics and Probability ,Asphalt pavement ,Scale (ratio) ,Artificial Intelligence ,Computer science ,021105 building & construction ,0211 other engineering and technologies ,General Engineering ,020101 civil engineering ,Geotechnical engineering ,02 engineering and technology ,0201 civil engineering - Abstract
Pavement crack assessment is an important indicator for evaluating road health. However, due to the dark color of the asphalt pavement and the texture characteristics of the pavement, current asphalt pavement crack detection technology cannot meet the requirements of accuracy and efficiency. In this paper, we propose an end-to-end multi-scale full convolutional neural network to achieve the semantic segmentation of cracks in road images by learning the crack characteristics in the complex fine grain background of asphalt pavement. The method uses DenseNet and deconvolution network framework to achieve pixel-level detection and fuses features learned from different scales of convolutional kernels through a full convolutional network to obtain richer information on multi-scale features, allowing more detailed representation of crack features in high-resolution images. And the back end joins the SVM classifier to achieve crack classification after crack segmentation. Then we create a road test standard data set containing 12 cracks and evaluate it on the data. The experimental results show that the method achieves good segmentation effect for 12 types of cracks, and the crack segmentation for asphalt pavement is better than the most advanced methods.
- Published
- 2021