Open Access

ARTICLE

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

by Ce Li, Xinyu Shang, Liguo Zhang, Feng Yang, Jing Zheng, Xianlei Xu

1 State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology, Beijing, 100083, China.
2 School of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing, 100083, China.
3 Computer Science & Engineering, Harbin Engineering University, Harbin, 150001, China.
4 Computer Science & Engineering, Hong Kong University of Science and Technology, Hong Kong.
5 Stanford University, Palo Alto, CA 94305-6104, USA.
* Corresponding Author: Ce Li. Email: celi@cumtb.edu.cn.

Computers, Materials & Continua 2019, 61(3), 1245-1260. https://doi.org/10.32604/cmc.2019.06159

Abstract

Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection module. In the system, we use a role encryption scheme for video collection, data uploading, and access security, and propose a hybrid information method for defect detection. The experimental results show that our approach is a scalable and efficient defection detection cloud system.

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Citations