YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection
This work addresses the problem of resource-intensive manual bridge crack detection for civil infrastructure management, though it is incremental as it builds on existing YOLOv5 methods.
The study tackled bridge crack detection by proposing a lightweight YOLOv5s-based model with GhostNet, Transformer, and BiFPN, achieving 42% fewer parameters and improvements of 8.5% in accuracy and 1.1% in mAP.
In response to the situation that the conventional bridge crack manual detection method has a large amount of human and material resources wasted, this study is aimed to propose a light-weighted, high-precision, deep learning-based bridge apparent crack recognition model that can be deployed in mobile devices' scenarios. In order to enhance the performance of YOLOv5, firstly, the data augmentation methods are supplemented, and then the YOLOv5 series algorithm is trained to select a suitable basic framework. The YOLOv5s is identified as the basic framework for the light-weighted crack detection model through experiments for comparison and validation.By replacing the traditional DarkNet backbone network of YOLOv5s with GhostNet backbone network, introducing Transformer multi-headed self-attention mechanism and bi-directional feature pyramid network (BiFPN) to replace the commonly used feature pyramid network, the improved model not only has 42% fewer parameters and faster inference response, but also significantly outperforms the original model in terms of accuracy and mAP (8.5% and 1.1% improvement, respectively). Luckily each improved part has a positive impact on the result. This paper provides a feasible idea to establish a digital operation management system in the field of highway and bridge in the future and to implement the whole life cycle structure health monitoring of civil infrastructure in China.