Spatial attention-based dual stream transformer for concrete defect identification

Abstract

This paper addresses the problem of automatic crack identification within building structures. Cracks can cause structural damage to a building, necessitating prompt repairs to preserve lives. Detecting structural defects is a difficult process, especially in tall towers. Machine learning and deep learning approaches automate damage identification and crack detection. Machine learning-based methods train models with manually produced features that may contain errors. Deep convolutional neural networks (DCNN), on the other hand, enhance performance by extracting high-dimensional features. However, DCNN algorithms for crack identification lack a global focus on features that can compromise performance. This study inserts a dual-stream transformer module into a single 2D convolution neural network (CNN) layer. The dual-stream block contains a semantic and pixels path. Precise views of local and global features at the pixel level can be obtained through two paths. This increases self-attention information distributed parallel along the semantic and pixel paths to train the model. Further, the DSTNet (Dual Stream Transformer Network) superiority is validated on the three datasets and achieved notable performance.