霍林生

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:土木工程系

学科:结构工程

办公地点:综合实验4号楼501

联系方式:0411-84706304

电子邮箱:lshuo@dlut.edu.cn

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Deep learning-based visual inspection for the delayed brittle fracture of high-strength bolts in long-span steel bridges

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论文类型:会议论文

发表时间:2019-01-01

收录刊物:EI、CPCI-S

卷号:11321

关键字:You Only Look Once; bolted connection; high-strength bolt; delayed fracture; structural damage detection; object detection; deep learning

摘要:The delayed brittle fracture of high-strength bolts in long-span steel bridges threatens the safety of the bridges and even lead to serious accidents. Currently, human periodic inspection, the most commonly applied detection method for this kind of high-strength bolts damage, is a dangerous process and consumes plenty of manpower and time. To detect the damage fast and automatically, a visual inspection approach based on deep learning is proposed. YOLOv3, an object detection algorithm based on convolution neural network (CNN), is introduced due to its good performance for the detection of small objects. First, a dataset including 500 images labeled for damage is developed. Then, the YOLOv3 neural network model is trained by using the dataset, and the capability of the trained model is verified by using 2 new damage images. The feasibility of the proposed detection method has been demonstrated by the experimental results.