Automatic Detection of Road Subsurface Distress via Curriculum Learning: Learn Like an Expert

The application of deep learning algorithms for subsurface distress detection using ground penetrating radar (GPR) data has seen extensive utilization. However, a significant impediment arises because of the challenge in acquiring ground-truth subsurface distress samples. This scarcity of labeled data leads to incomplete training of deep learning algorithms and gives rise to a critical concern with respect to over-fitting. Generating additional samples through numerical simulation constitutes one of the most efficient methods to overcome insufficient GPR training samples. If both real and simulated samples are mixed for training, the deep learning model may miss the complexities in the samples and their learning state. Concurrently, the presence of noise and anomalous samples might lead the model to converge toward a suboptimal local minimum. This phenomenon is particularly conspicuous in the field of GPR because of the stochastic and disordered propagation of radar waves, resulting in amplified noise and abnormal samples. A robust curriculum learning algorithm, inspired by expert training methods, was created to train models from simple simulated samples to complex field samples. This strategy evaluates the performance of two object detection models, YOLOv7 and Faster R-CNN, under the proposed framework. Compared to the model trained from the whole datasets out of order, the precision, recall, F1_score, and mean average precision are all improved. The results demonstrate that the proposed method can enhance the model’s precision by approximately 8% and recall by about 11% under the same dataset. These findings highlight its great potential for expediting convergence and boosting the overall model performance.