Effect of Data Augmentation on Spalling Condition Classification using Deep Transfer Learning
محل انتشار: ششمین همایش بین المللی مهندسی سازه
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 283
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شناسه ملی سند علمی:
ISSEE06_055
تاریخ نمایه سازی: 16 بهمن 1401
چکیده مقاله:
Image processing is gaining attention in many structural research fields including condition assessment of concrete structural members using photos. on the other hand, the image processing field is completely revolutionized thanks to deep learning (DL). This research applies data augmentation besides deep learning technology to a civil engineering application, namely detecting the spalling condition of structural components from images. For this purpose, a dataset is used that was published by the structural image net project and only has a limited amount of images. Transfer Learning (TL) based on VGGNet (Visual Geometry Group) is presented and used on the mentioned dataset to minimize overfitting. Furthermore, a comprehensive data augmentation is also carried out. The algorithm boosted with data augmentation provides good recognition performance in spalling condition detection compared to training without data augmentation. These findings also point out the possible use of deep Learning in structural recognition tasks with limited data.
کلیدواژه ها:
نویسندگان
Sadeq Kord
P.hD. Student, Department of Civil Engineering, Amirkabir University of Technology, Tehran, Iran,
Touraj Taghikhany
Associate Professor, Department of Civil Engineering, Amirkabir University of Technology, Tehran, Iran