To effectively detect the surface cracks of subway tunnels, an automatic tunnel crack detection system based on machine vision is presented. Aiming at the problems of environmental complexity and low contrast in subway tunnels, the image texture feature is first enhanced by the methods of frequency domain filtering and spatial differencing. Then, depending on the characteristics of the tunnel cracks in question, the crack propagation method is used to extract the complete cracks. Finally, broken cracks are connected during processing, and the method of combining projection and threshold is used to determine the crack types. At the same time, characteristics such as the length, width, and area of the cracks are obtained. The experimental results show that the presented methods can effectively extract complete cracks in complex tunnel environments. The identification error of tunnel crack parameters meets the actual engineering requirements.
The accuracy of the detection in the image-based subgrade settlement monitoring system depend on the quality of the collected spot image, This paper studies the main factors affecting the quality of spot images and propose a method for quality assessment of spot image without reference. The method analyze the characteristics of the spot change under the influence of external conditions, and reflect the change effect of the spot feature through the size, brightness, edge definition and contrast of the spot image.Then,the four evaluation factors are integrated into one. Through the experiment, the spot images under the influence of the target surface are collected. The spot image obtained by the experiment is evaluated using the non-reference spot image evaluation method proposed in this paper, and verifies by measuring the center position of the spot image. The experimental results show that the spot image is affected by different influencing factors, this evaluation method method can combine various influencing factors to reflect the change of image quality,and can give reasonable evaluation results for spot images that are affected to varying degrees. It provides theoretical and experimental basis for the actual subgrade surface settlement monitoring system.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.