Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network
This work addresses the problem of automated pavement crack detection for infrastructure maintenance, but it is incremental as it builds on existing CNN approaches with a specific data handling improvement.
The paper tackles automated pavement crack detection by proposing a supervised deep learning method using a convolutional neural network (CNN) that handles imbalanced data through a sample ratio modification strategy. It outperforms five existing methods on two public databases.
Automated pavement crack detection is a challenging task that has been researched for decades due to the complicated pavement conditions in real world. In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions. Specifically, a convolutional neural network (CNN) is used to learn the structure of the cracks from raw images, without any preprocessing. Small patches are extracted from crack images as inputs to generate a large training database, a CNN is trained and crack detection is modeled as a multi-label classification problem. Typically, crack pixels are much fewer than non-crack pixels. To deal with the problem with severely imbalanced data, a strategy with modifying the ratio of positive to negative samples is proposed. The method is tested on two public databases and compared with five existing methods. Experimental results show that it outperforms the other methods.