By Topic

Automatic Recognition of Pavement Surface Crack Based on BP Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Guoai Xu ; Digital Content Res. Center, Beijing Univ. of Posts & Telecommun., Beijing ; Jianli Ma ; Fanfan Liu ; Xinxin Niu

Pavement distress detection is the base of highway maintenance. With crack being the main distress in the actual pavement surface, digital image processing has been widely applied to cracking recognition recently. This paper presents a novel artificial neural network based pavement cracking recognition method in the area of image processing. The novelty of our approach is to utilize self-studying feature of neural network to complete the cracking identification. By converting cracking recognition to the cracking probability judgment for every sub-block image, cracking trend could be calculated, and a method for revising the neural network output is proposed to increase accuracy of identification. Actual pavement images are used to verify the performance of this method, and the results show that the surface crack could be identified correctly and automatically.

Published in:

Computer and Electrical Engineering, 2008. ICCEE 2008. International Conference on

Date of Conference:

20-22 Dec. 2008