CrackNet: A novel multi-scale architecture for crack segmentation

Abstract

Crack detection is essential for structural safety inspection but remains challenging due to noise, illumination variations, and complex backgrounds. In this paper, we propose CrackNet, a segmentation network specifically designed for concrete crack detection. CrackNet integrates three key modules: a lightweight multi-scale convolution enhancement block (LightMSCBlock) in the encoder to capture both local details and global context, a SAF attention module embedded in skip connections for scale-aware feature fusion and edge refinement, and a multi-scale feature fusion (MSFF) module in the decoder to enhance feature integration while reducing information loss. Extensive experiments on three public datasets—CFD, Crack500, and DeepCrack—demonstrate that CrackNet consistently outperforms state-of-the-art methods. Specifically, on CFD, F1 and IoU improve by 6.37% and 7.1% over SegFormer; on Crack500, F1 increases by 3.86% compared with MobileNetV3-UNet; and on DeepCrack, F1 and IoU gains reach 5.7% and 2.5%, respectively. Ablation studies further confirm the complementary effectiveness of LightMSCBlock, SAF, and MSFF. Overall, CrackNet achieves superior accuracy and robustness, showing strong potential for real-world engineering applications. The code is available at the following link: https://github.com/xzz-ya/CrackNet.git