Parallel Systems for the Bridge Inspection
Hongyao Ma, Zhixue Wang, Hang Gao, Zhen Shen, Hong Zhang, Xueliang Hu, Chuanfu Li, Gang Xiong
Abstract
As the number of bridges grows in China, bridge inspection is very necessary to ensure public transport safety. With the development of various technologies in recent years, such as UAV (Unmanned Aerial Vehicle), computer vision, advanced sensing, artificial intelligence and so on, intelligent technologies in bridge inspection have developed rapidly and are gradually replacing traditional methods. Here we proposed parallel systems for bridge inspection, which introduces parallel theory into the field of bridge inspection to solve the problems of dataset shortage and special scene prediction. Based on the classification dataset (CCD) and parallel classification dataset (PCD), ConvNeXt and other networks are trained and compared. The final crack identification accuracy reached 99.22%. We believe that the framework proposed in this paper can improve the efficiency and accuracy of bridge inspection significantly.