Modification and Evaluation of Attention-Based Deep Neural Network for Structural Crack Detection

Abstract

Cracks are one of the safety-evaluation indicators for structures, providing a maintenance basis for the health and safety of structures in service. Most structural inspections rely on visual observation, while bridges rely on traditional methods such as bridge inspection vehicles, which are inefficient and pose safety risks. To alleviate the problem of low efficiency and the high cost of structural health monitoring, deep learning, as a new technology, is increasingly being applied to crack detection and recognition. Focusing on this, the current paper proposes an improved model based on the attention mechanism and the U-Net network for crack-identification research. First, the training results of the two original models, U-Net and lrassp, were compared in the experiment. The results showed that U-Net performed better than lrassp according to various indicators. Therefore, we improved the U-Net network with the attention mechanism. After experimenting with the improved network, we found that the proposed ECA-UNet network increased the Intersection over Union (IOU) and recall indicators compared to the original U-Net network by 0.016 and 0.131, respectively. In practical large-scale structural crack recognition, the proposed model had better recognition performance than the other two models, with almost no errors in identifying noise under the premise of accurately identifying cracks, demonstrating a stronger capacity for crack recognition.

Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network

“…Wang et al [27] proposed an efficient mobile attention X-network, MA-Xnet, for crack detection based on two models, U-Net and Dual Attention Network (DANet). Yuan et al [28] improved U-Net and proposed ECA-UNet, which has better recognition performance than the other two models in largescale structural crack recognition. Zhu et al [29] proposed a method for calculating the geometric features of cracks based on the concepts of skeleton extraction and function fitting.…”

Research on road damage recognition and classification based on improved VGG-19

“…However, among many neural networks, VGG-19 (Visual Geometry Group-19, VGG-19) can deal with image classification problems well due to its own structural characteristics. Moreover, in recent years, VGG-19 has achieved remarkable results in the field of structural damage identification, such as medicine, civil engineering, computer and other fields [24][25][26]. However, there are few studies in the field of pavement disease identification.…”