Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/78138
Title: Spalling detection via deep transfer network
Authors: Wu, Ruida
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Abstract: A spall is defined as flakes materials that are broken off of a large solid body. Spalling is a significant surface hazard that can undermine the durability of concrete structure and thus may affect structural safety of the building and sometimes cause serious construction problems with injuries and loss of properties. A number of image processing techniques (IPTs)[1] have been implemented for detecting civil infrastructure defects. These IPTs are able to extract flaw parts such as cracks from images, but face challenges while analyzing images under various environmental factors (e.g. lightening, shadow). In order to get a better performance in detecting civil infrastructure problem, a method based on deep learning for spalling detection could be developed. Due to the fast development of artificial intelligence and machine learning technologies, especially in applications of deep learning in computer vision, structural damage recognition could rely on such knowledge because such problem is indeed a repetitive work which requires learning knowledge from past experience. In this project, we will apply technologies related to knowledge transfer to develop a model that does spall recognition on images taken by Unmanned Aerial Vehicle (UAV) with high accuracy. In order to improve the model, we will do transfer learning, data assemble and knowledge distillation to this model so that it is portable as well as robust enough.
URI: http://hdl.handle.net/10356/78138
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
WuRuida_FYP_final_report.pdf
  Restricted Access
5.21 MBAdobe PDFView/Open

Page view(s) 50

34
checked on Oct 20, 2020

Download(s) 50

8
checked on Oct 20, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.