Pavement crack detection combining non-negative feature with fast LoG in complex scene

Wanli Wang; Xiuhua Zhang; Hanyu Hong

doi:10.1117/12.2205292

14 December 2015 Pavement crack detection combining non-negative feature with fast LoG in complex scene

Wanli Wang, Xiuhua Zhang, Hanyu Hong

Proceedings Volume 9812, MIPPR 2015: Automatic Target Recognition and Navigation; 98120L (2015) https://doi.org/10.1117/12.2205292
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Pavement crack detection is affected by much interference in the realistic situation, such as the shadow, road sign, oil stain, salt and pepper noise etc. Due to these unfavorable factors, the exist crack detection methods are difficult to distinguish the crack from background correctly. How to extract crack information effectively is the key problem to the road crack detection system. To solve this problem, a novel method for pavement crack detection based on combining non-negative feature with fast LoG is proposed. The two key novelties and benefits of this new approach are that 1) using image pixel gray value compensation to acquisit uniform image, and 2) combining non-negative feature with fast LoG to extract crack information. The image preprocessing results demonstrate that the method is indeed able to homogenize the crack image with more accurately compared to existing methods. A large number of experimental results demonstrate the proposed approach can detect the crack regions more correctly compared with traditional methods.

Citation Download Citation

Wanli Wang, Xiuhua Zhang, and Hanyu Hong "Pavement crack detection combining non-negative feature with fast LoG in complex scene", Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 98120L (14 December 2015); https://doi.org/10.1117/12.2205292

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