Method for detecting road pavement damage based on deep learning

Jiaqi Li; Xuefeng Zhao; Hongwei Li

doi:10.1117/12.2514437

1 April 2019 Method for detecting road pavement damage based on deep learning

Jiaqi Li, Xuefeng Zhao, Hongwei Li

Proceedings Volume 10972, Health Monitoring of Structural and Biological Systems XIII; 109722D (2019) https://doi.org/10.1117/12.2514437
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

Abstract

A safe and healthy road condition plays a supporting role in the public travel and the national economy. Therefore, effective management and maintenance methods have become the key problems that the researchers and engineers are urgently solving, early damage detection and warning are also important for disaster emergency treatment, but some traditional road damage identification methods are often costly and need to be equipped with professional persons. Due to the complexity of pavement conditions, some existing defects datasets are not perfect, although the accuracy is high, they cannot be put into practical use. Based on the object detection technology of deep learning, the author introduced a novel method which is more effective and relatively cheap. In this paper, 5966 images with road damage of different angles and distances were collected, and the damage categories included Lateral Crack, Longitudinal Crack, Pothole and separation, Alligator Crack, and Damage around the well cover which had never been considered in the datasets in any researches. After training with GPU using convolutional neural network, the average precision can reach 96.3%.

Citation Download Citation

Jiaqi Li, Xuefeng Zhao, and Hongwei Li "Method for detecting road pavement damage based on deep learning", Proc. SPIE 10972, Health Monitoring of Structural and Biological Systems XIII, 109722D (1 April 2019); https://doi.org/10.1117/12.2514437

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available