A Novel Lightweight U-Shaped Network for Crack Detection at Pixel Level

Abstract

Cracks are the most prevalent form of damage on pavement surfaces. Accurately recognizing pavement cracks is often difficult due to background interference and other challenges. Moreover, accurate and fast automatic detection of road cracks plays a crucial role in assessing pavement conditions. Therefore, a highly efficient lightweight network with only 0.78M parameters is proposed for the pixel-level pavement crack detection task. In this paper, adaptive enhancement module (AEM) is designed and added to the encoder network in order to avoid the problem of insufficient model learning capability due to the use of depth-wise separable convolutions. Meanwhile, a coordinate-aware fusion module (CFM) is proposed, which fully fuses skip connection features and decoder features to enhance cross-channel interaction information. Comprehensive experimental results demonstrate that the proposed network outperforms existing methods across four public datasets: CamCrack789, CFD, DeepCrack237, and Crack500, achieving F1 scores of 94.6%, 92.8%, 91.0%, and 79.8%, respectively. Furthermore, ablation study confirmed the efficacy of both the AEM and the CFM.

Keywords

• Crack detection
• pixel-level
• deep learning
• convolution neural networks