Research on Intelligent Identification Technology for Bridge Cracks

The infrastructure construction of bridges is growing rapidly, and the quality of concrete structures is becoming increasingly stringent. However, the issue of cracks in concrete structures remains prominent. In on-site bridge crack detection, the traditional crack identification techniques fail to meet demands due to their inefficiency, inaccuracy, and the challenges posed by high-altitude conditions. In response to this, this paper proposes a bridge crack multi-task integration algorithm based on YOLOv8 object detection and DeepLabv3+ semantic segmentation. This integrated approach offers advantages such as high precision and low inference time. Testing wall cracks using this method, compared to the original approach, resulted in a 10.18% improvement in IOU and a 9.64% improvement in the F1 score. Regarding the detection model, it was deployed on edge computing devices. By applying the TensorRT inference acceleration framework, the camera FPS increased to 9.66, a 59.97% improvement compared to the version without the acceleration framework. This enabled accurate, real-time bridge crack detection on the edge computing devices. Furthermore, the edge computing device was also applied in a self-developed drone, which was tested on-site at the Donghai Bridge, providing a new solution for safe and reliable structural inspection.