Vision-Based Pipe Monitoring Robot For Crack Detection Using Canny Edge Detection Method as an Image Processing Technique

Vision-Based Pipe Monitoring Robot For Crack Detection Using Canny Edge Detection Method as an Image Processing Technique

Nur Mutiara Syahrian, Pola Risma, Tresna Dewi


Piping setup is very important to ensure the safety and eligibility of the piping system before applied in industry. One of the techniques to facilitate perfect piping setup is by employing pipe monitoring robot. Pipe monitoring robot is designed in this research to monitor cracks or any other defects occur inside a pipe. This automatic monitoring is conducted by the application of image processing with canny edge detection. Canny edge detection method detects the edges or lines of any cracks inside the pipe and processes them to create differences in image, therefore only the cracks can be shown and finally, those cracks can be well analyzed. Canny edge detection has 5 processing techniques that are smoothing, finding gradients, non-maximum suppression, double thresholding, and edge tracking by hysteresis. In this research, the experiment was conducted by letting a robot monitoring new pipe and detecting cracks. Two cracks samples were taken and analyzed. The results show that the best value for smoothing is 10 and 5 for thresholding in getting not too blurred or to sharp result.


image processing, edge detection, algoritma canny

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[1] C. Anindya, S. Fuada, S. Firmansyah, and D. Lestari, “Design of Pipe Tracking Robot Using Electric Nose technology as a Leakage Detector," Student Creativity Program - Karsa Cipta, Department of Electrical Engineering Education FT UNM, 2011.

[2] T. H. Dinh, Q. P. Ha, and H. M. La, “Computer Vision-Based Method for Concrete Crack Detection,” in 2016 14th International Conference on Control, Automation, Robotics & Vision (ICARCV), Thailand., 2016.

[3] A. Mohan and S. Poobal, “Crack Detection Using Image Processing: A Critical Review and Analysis,” Alexandria Engineering Journal, Feb. 2017.

[4] T. Sutoyo, E. Mulyanto, V. Suhartono, and Wijanarto, "Theory of Digital Image Processing," Yogyakarta, 2009.

[5] C. Danil, “Edge Detection suing Canny Algorrithm,” STMIK IBBI, 2011.

[6] A. Y. Zunaidi, “Design of Empty Parking Space Based on Digital Image," Pp. 5-17, 2011.

[7] S. Riyanto, "Image Processing Practice," Politeknik Elektronika Negeri Surabaya, 2006.

[8] F. Astuti, "Digital Image Processing: Concept and Theory," Yogyakarta: ANDI Publisher, 2013.

[9] R. Munir, “Digital Image Processing using Algorithm Approach," Informatika Bandung, 2004.

[10] U. Ahmad, "Image Processing and Its Programming Tachniques," Yogyakarta: Graha Ilmu, 2005.

[11] A. Basuki, F. Palandi, Jozua, and Fatchurochman, "Digital Image Processing Using Visual Basic," Yogyakarta: Graha Ilmu, 2005.

[12] R. C. Gonzalez and R. E. Richard E. Woods, "Digital Image Processing," New Jersey: Prentice-Hall, 2009.

[13] R. Sigit, D. Pramadihanto, and R. Sulaiman, “Facial Recognition System in Real Time," Industrial Electronic Seminar (IES) Proceeedings, 2005.

[14] L. B. Ethes, P. Menezes, F. Lerasle, and J. Hayet, “Face Tracking and Hand Gesture Recognition For Human-Robot Interaction.”


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