Abstract
Pavement needs to be maintained from the moment its service life begins. The maintenance strategy is mainly based on pavement quality indexes, such as the road damage rate (DR). Accurate pavement distress detection is generally costly and complicated. An inappropriate pavement distress detection strategy could yield a low efficiency of budget usage and untreated road diseases. This study describes an innovative vision-based pavement crack detection strategy that provides a direct pavement surface condition index (PCI) for a specific pavement location. This strategy was achieved with the application of the convolutional neural network (CNN) algorithm to mine a database that contains over five thousand pavement distress images to classify the pavement crack type. To improve the accuracy of the network, a genetic algorithm (GA) was employed to optimize the model. To simultaneously consider the efficiency and accuracy, an enhanced image processing method was used to measure the DR of the pavement. The model input image size is 100 × 100 pixels and the test results showed that the proposed method has satisfactory performance in robustness, accuracy, and processing speed. The accuracy of classifying different crack types reached 98%, and the processing time for an image is 0.047 s. This research, for the first time, proposed an ellipse fitting method to calculate the block of mesh cracks. The overall accuracy in detecting the pavement damage rate reached 90%. This study demonstrates the potential of an innovative deep learning method in automated pavement quality index calculations to provide direct guidance for long-term asphalt pavement optimal rehabilitation and maintenance (R&M) decisions.
Published Version
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