Innovative method for recognizing subgrade defects based on a convolutional neural network

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Subgrade defects originate below the base of an asphalt pavement and they contribute significantly to pavement damage. The detection of subgrade defects is considered challenging because the recognition of defects is difficult. Therefore, the utilization of ground penetrating radar (GPR) to detect subgrade defects has attracted significant interest in recent years. However, the use of manually processed GPR images for classifying defects is inefficient and inaccurate. Thus, in this study, we applied convolutional neural networks (CNNs) to GPR images for automatically classifying subgrade defects (e.g., uneven settlement, sinkholes, and subgrade cracks). Two CNNs called multi-stage CNN and cascade CNN with different structures were established to accomplish the tasks automatically. The main difference between the two CNNs is that the cascade CNN is a classifier 2, which is for recognition and trained only using hard samples. Each CNN was developed in training, validation, and testing processes. Based on the training and testing results, sensitivity analysis was performed to verify the stability of the CNNs. We compared state-of-the-art methods for defect detection and the CNN-based method in order to verify the superior performance of the CNNs. Finally, we tested an application of the CNN-based method to show that it is transferrable to other asphalt pavements. The training results indicated that the cascade CNN classified subgrade defects with 97.35% accuracy during training and 96.80% in validation, while the multi-stage CNN classified subgrade defects with 91.35% accuracy during training and 90.45% in validation. The sensitivity analysis results showed that the cascade CNN exhibited the expected stability in terms of the transmitting frequency, i.e., the frequency of a high-frequency electromagnet wave from the transmitting antenna of the GPR, and different highway structures, whereas the multi-stage CNN did not. In addition, compared with Sobel edge detection and K-value clustering analysis, the CNN-based method obtained more robust performance at subgrade defect detection under various conditions using raw images. These results indicate that the CNN-based method performs well and it can classify subgrade defects in realistic situations.