Dual-path network combining CNN and transformer for pavement crack segmentation

Cracks are one of the most common pavement surface diseases. Timely repair of these cracks is imperative to prevent a substantial reduction in the pavement's service life. However, the persistent challenges in crack segmentation arise from factors such as thin and shallow crack characteristics, a cluttered background, and foreground distractors. In response to these challenges, a dual-path network for pavement crack segmentation is introduced, leveraging a synergistic combination of Convolutional Neural Network (CNN) and transformer. First, the proposed approach involves a lightweight CNN encoder for local feature extraction and a novel transformer encoder integrating a fully convolutional high-low frequency attention (FCHiLo) mechanism and an efficient feedforward network for global feature extraction. Second, a complementary fusion module (CFM) is introduced to aggregate intermediate features extracted from both encoders. The multi-scale fusion outputs are systematically conveyed to the decoder, facilitating gradual image recovery and segmentation result acquisition. Evaluation on three publicly available datasets-DeepCrack, CrackForest, and CrackTree 260-affirms the superior performance of the proposed network compared to ten established models.

Automated summary

This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.