Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN: Fid-Bau Portal

Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN

Li, Ruoxian / Yu, Jiayong / Li, Feng / Yang, Ruitao / Wang, Yudong / Peng, Zhihao

Highlight • Faster R-CNN for bridge crack detection was combined with Unmanned Aerial Vehicles (UAVs) • A set of verification experiments was done to decide the distance range for stable imaging of three types of UAVs. • A Two-RTSs system for UAV hovering accuracy was proposed, and the displacement and attitude angle changes of UAV were acquired. • Our method with bridge crack detection can achieve precision, recall and F1-score of 92.03 %, 96.26 %, and 94.10 %, respectively.

Abstract In order to improve the efficiency and accuracy of bridge crack detection, this paper introduces Deep Learning for bridge crack detection with Unmanned Aerial Vehicles (UAVs). In this study, a method for deciding the distance range for stable imaging was used, and we have done a set of verification experiments on three types of UAVs. Then a Two-RTSs system for UAV hovering accuracy was proposed, and the displacement and attitude angle changes of UAV were acquired. The results indicated that DJI M210-RTK could acquire high-quality images at longer distances and had better stability in hover mode. So, we decided to use the DJI M210-RTK for bridge crack recognition. Finally, the Faster Region Convolutional Neural Network (Fatesr R-CNN) algorithm based on VGG16 transfer learning is used for the apparent detection of a bridge located in Hunan Province. Our method can achieve precision, recall and F1-score of 92.03 %, 96.26 %, and 94.10 %, respectively. The outcome of this study shows that automatic bridge crack detection using UAVs and Faster R-CNN can be more efficient while maintaining high accuracy.