Intelligent identification of pavement cracks based on PSA-Net - Lin, Xuan; Zhang, Jian; Wu, Daifeng; Meng, Enhong; Liu, Maoyi; Li, Meng; Gao, Fuliu (Frontiers in Environmental Science, 2023)
The identification of pavement cracks is critical for ensuring road safety. Currently, manual crack detection is quite time-consuming. To address this issue, an automated pavement crack-detecting technology is required. However, automatic pavement crack recognition remains challenging owing to the intensity heterogeneity of cracks and the complexity of the backdrop, e.g., low contrasting of damages and backdrop may have shadows with comparable intensity. Motivated by breakthroughs in deep learning, we present a new network architecture combining the feature pyramid with the attention mechanism (PSA-Net). In a feature pyramid, the network integrates spatial information and underlying features for crack detection. During the training process, it improves the accuracy of automatic road crack recognition by nested sample weighting to equalize the loss caused by simple and complex samples to the loss. To verify the effectiveness of the suggested technique, we utilize a dataset of real road cracks to test it with different crack detection methods. Experimental results show that the offered technique outperforms established methods in terms of accuracy and generality.