BFMPF-Net: Bidirectional Frequency-Domain Modulation Progressive Fusion Network for Road Crack Segmentation

Abstract

Recently, deep learning–based methods for road crack segmentation have achieved promising performance, particularly in robotic vision applications such as automated inspection and maintenance. However, most frequency-domain methods employ a decoupled processing strategy, overlooking the dynamic modulation mechanism between high- and low-frequency components, which constrains the model's effectiveness in detecting cracks within complex environments. Moreover, existing methods suffer from low information fidelity during feature transmission, where critical encoder details are progressively lost in the decoder, making it difficult to reconstruct complete crack structures. To address these issues, we propose a Bidirectional Frequency-domain Modulation Progressive Fusion Network (BFMPF-Net). Specifically, we propose a Bidirectional Frequency-domain Modulation Enhancement (BFME) module that effectively exploits bidirectional modulation between high- and low-frequency components and learns the spatial weights of high-frequency features to attenuate noise and preserve crack edge details, thereby improving the performance of crack segmentation. Furthermore, the Progressive Guidance Fusion module serves as another core component of our framework. It leverages the spatial prior provided by the original low-resolution image to guide feature refinement via stepwise optimization from coarse contours to fine edges, thereby ensuring the integrity of crack segmentation. Evaluation on three publicly available datasets—CrackTree260, CrackLS315, and Crack760—affirms the superior segmentation accuracy of the proposed BFMPF-Net compared to current mainstream methods.