Intelligent Surface Cracks Detection in Bridges Using Deep Neural Network
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Published:2023-07-06
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Volume:
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ISSN:0219-4554
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Container-title:International Journal of Structural Stability and Dynamics
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language:en
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Short-container-title:Int. J. Str. Stab. Dyn.
Author:
Zhang Xiaobo12,
Luo Zhipeng1,
Ji Jinghao1,
Sun Yimin1,
Tang Haihao1,
Li Yongle2
Affiliation:
1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, P. R. China
2. School of Civil Engineering, Southwest Jiaotong University, Chengdu 611756, P. R. China
Abstract
Cracking is one of the most common bridge diseases. If bridge cracks are not repaired: in time, they may cause gradual changes to the concrete structure, which can seriously affect its strength. A network called YOLOv5-TS is what we suggest to detect intelligently bridge surface cracks in images. To improve the network performance, we integrate SPPCSPC into the original YOLOv5 network to ensure adaptive image output and obtain receptive fields of various sizes. Meanwhile, transposed convolution is incorporated to improve the capacity of the network for learning weights on its own and reduce characteristic information loss. In response to the diverse morphology of bridge cracks, cracks are identified according to their mechanical causes crack inclination, and divided into four categories: horizontal cracks (0[Formula: see text]–20[Formula: see text]), low-angle cracks (20[Formula: see text]–45[Formula: see text]), vertical cracks (70[Formula: see text]–90[Formula: see text]) and high-angle cracks (45[Formula: see text]–70[Formula: see text]). Experiments on the ZJU SYG crack data set confirm that the proposed YOLOv5-TS has a better crack intelligent identification effect on bridge surface images than other compared baselines. The best performance of YOLOv5-TS is found in mAP@0.5 (0.752), mAP@0.5:0.95 (0.518), and recall (0.794), thus demonstrating the model’s practical value.
Funder
National Natural Science Foundation of China
Key Research and Development Program in Sichuan Province of China
Fundamental Research Funds for the Central Universities
Publisher
World Scientific Pub Co Pte Ltd
Subject
Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Building and Construction,Civil and Structural Engineering