Real-Time Concrete Damage Detection Based on Deep Learning Technique

Abstract

Visual inspection is one of the commonest approaches in the field of Structural Health Monitoring (SHM) However the works rely heavily on the inspectors’ knowledge and experience leading to subjective assessments On the other hand with the rapid development of the Convolution Neural Network (CNN) deep learning technique has been widely adopted for damage detection In this study a real-time concrete surface damage detecting system was developed based on YOLOv3 network The influences of using different types of datasets for training on the accuracy of the models was also investigated The study is divided into three parts: image video and real-time objects detection First an image classification model is developed for the recognition of the cracked and uncracked concrete images Second for the object detection in video YOLOv3 was used for the crack and spalling detection using different types of datasets The model with the best performance was therefore adopted for the real-time surface damage detection Consequently four locations in Tainan City were selected for the validation of the real-time damage detection model The results show that the real-time damage detection model has an outstanding performance with an AP of 79 78% in the detection of concrete crack and AP of 81 35% for the exposed rebar damages

Date of Award	2020
Original language	English
Supervisor	Hsuan-Teh Hu (Supervisor)