An efficient semantic segmentation method for road crack based on EGA-UNet

Road cracks affect traffic safety. High-precision and real-time segmentation of cracks presents a challenging topic due to intricate backgrounds and complex topological configurations of road cracks. To address these issues, a road crack segmentation method named EGA-UNet is proposed to handle cracks of various sizes with complex backgrounds, based on efficient lightweight convolutional blocks. The network adopts an encoder-decoder structure and mainly consists of efficient lightweight convolutional modules with attention mechanisms, enabling rapid focusing on cracks. Furthermore, by introducing RepViT, the model's expressive ability is enhanced, enabling it to learn more complex feature representations. This is particularly important for dealing with diverse crack patterns and shape variations. Additionally, an efficient global token fusion operator based on Adaptive Fourier Filter is utilized as the token mixer, which not only makes the model lightweight but also better captures crack features. Finally, to demonstrate the method's effectiveness and accuracy, we compare the proposed approach with some existing methods on three public datasets. Experimental results demonstrate that the proposed method outperforms existing approaches in detecting cracks of diverse shapes and sizes within complex backgrounds, satisfying the requirements for both high precision and real-time segmentation.