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

Abstract

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.