Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)                   IJEEE 2025, 21(2): 3615-3615 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mohd Ideris N S, Ali H, Zanar Azalan M S, Tengku Amran T S. Hyperbolic Detection of Ground Penetrating Radar for Buried Pipes Utilities Using Viola Jones. IJEEE 2025; 21 (2) :3615-3615
URL: http://ijeee.iust.ac.ir/article-1-3615-en.html
Abstract:   (201 Views)
GPR (Ground Penetrating Radar) is well-known as an effective non-invasive imaging approach for shallow nature underground discovery, like finding and locating submerged objects. Although GPR has achieved some success, it is difficult to automatically process GPR images because human experts must interpret GPR images of buried objects. This can happen due to the possibility of a variety of mediums or underground noises from the environment, especially rocks and roots of trees. Thus, detecting hyperbolic echo characteristics is critical. As a result, Viola Jones detection is used to determine whether the presence of a hyperbolic signature underground indicates a pipe or not. GPR can also be used in the public works department because it is a non-destructive tool. Workers, for example, should be aware of the pipe size that must be replaced when it leaks. The original GPR image already shows hyperbolic image distortion due to pipe refraction. The current method is unreliable due to its lack of flexibility. As a result, there is another method for resolving this issue. Thus, the image will be pre-processed to eliminate or reduce background noise in the GPR input image. The results of this project demonstrate that the Viola Jones algorithm can accurately detect hyperbolic patterns in GPR images.
Full-Text [PDF 618 kb]   (26 Downloads)    
Type of Study: Only For Articles of ELECRiS 2024 | Subject: Antenna
Received: 2024/12/18 | Revised: 2025/03/13 | Accepted: 2025/03/01

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.