A road crack detection method based on improved U-Net

Abstract

Cracks in infrastructure engineering projects have a significant impact on the performance and functionality of roads, bridges, and buildings. Traditional crack detection methods are time-consuming and subjective, thus calling for faster and more accurate detection methods. This paper proposes a novel algorithm called the Multi-Layer Supervised Network, which combines the U-Net model and deformable convolution to address the challenges in crack detection. Our method utilizes the encoder-decoder architecture of the U-Net model to extract crack features and addresses the issues of gradient vanishing and overfitting through a multi-layer supervised mechanism. Meanwhile, the deformable convolution is introduced to be able to deal with different scales and angles of cracks adaptively and improve the feature expression capability. The experimental results demonstrate that our proposed method can accurately detect and segment cracks in images with good generalization ability. Compared to traditional image classification and object detection methods, our approach can extract more accurate crack information, providing valuable insights for the evaluation and maintenance of infrastructure engineering projects.