A novel transformer model for surface damage detection and cognition of concrete bridges

Abstract

Bridges play an important role in modern transportation systems and road networks, and hence it is essential to use various models based on visual inspection to detect and prevent the damages on the surface of bridge structure. However, due to the limitation of traditional models or lack of modelling data, bridge damages are often difficult to be accurately detected. This paper proposed a novel deep learning model called Bridge Detection Transformers (BR-DETR) based on Detection Transformers (DETR). Through analysis of existing bridge damage instances, we used a copy-paste data augmentation method to create new samples and significantly increased the sample size. Convolution was replaced by Deformable Conv2D, which introduces two-dimensional offsets to the regular grid sampling positions of standard convolution. Convolutional Project Attention was also added after the self-attention layer, which enabled additional modeling of local spatial context. In each encoder and decoder layer, Locally-enhanced Feed-Forward (LeFF) was used to replace the Feedforward to promote the correlation between adjacent tokens in the spatial dimension. The BR-DETR model outperformed the DETR model in detection performance with increased mAP and recall on the augmented highway bridge damage dataset and on the augmented Shandong bridge damage dataset.