High–low frequency feature fusion network for pavement crack segmentation in complex environments
Year: 2025
Type: article
Abstract: For routine road maintenance, accurate and efficient crack detection holds significant research value and practical importance. With advances in computer vision, image processing-based crack detection has achieved promising results. However, in complex environments, factors such as illumination and weather introduce noise interference, especially in high-resolution crack images where fine fracture features show heightened complexity and diversity. This often leads to degraded detection accuracy in such challenging scenarios. To address this, we propose FE-SegNeXt, a convolutional neural network segmentation framework tailored for high-resolution crack detection in complex environments. The algorithm leverages distinct characteristics of high- and low-frequency features in crack images: average pooling separates these features, while a dedicated Frequency Collaborative Enhancement Module (FCEM) independently enhances high- and low-frequency components before fusing multi-band information. This design significantly improves the model’s ability to extract subtle cracks from high-resolution images under noisy conditions. Additionally, we introduce a Locally Enhanced Feed-Forward Network (LE-FFN) to amplify perception of weak crack signals in local regions, further refining fine-grained feature extraction. Experimental results on the public datasets Sun520, Rain365, and BJN260 demonstrate that the proposed method achieves F1-scores of 61.55%, 62.57%, and 60.31%, respectively, outperforming existing crack detection algorithms. • FE-SegNeXt detects fine cracks in high-res road images via frequency-aware feature fusion. • A dual-branch design captures edges and global context from high- and low- frequency features. • LFFN expands receptive fields and enhances detail for subtle and broken crack detection.
Authors: Huazhong Jin, Guofeng Wu, Yi Cen
Institution: Hubei University of Technology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,550
OpenAlex ID: W4417485257