Exploration of Pipelines from the use of GPR Data by Neural Network
Kode Rajiv1, Booba Basaveswara Rao2, G Ramesh Chandra3, N V Ganapathi Raju4

1Kode Rajiv, Associate Professor, Department of Information Technology, GRIET, Hyderabad (Telangana), India.
2Booba Basaveswara Rao, Department of Computer Center, ANU, Guntur (Andhra Pradesh), India.
3G Ramesh Chandra, Ph.D, Department of CSE, JNTUH, Hyderabad (Telangana), India.
4N V Ganapathi Raju, Department of I.T, GRIET, Hyderabad (Telangana), India.
Manuscript received on 20 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3711-3715 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14700982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1470.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Noticing about the buried pipes is a important issue, In many regions of the world. In spite of the fact that several techniques are there. This literature is used to find out the underground pipes automatically that provides accuracy execution is underway. Which gave amazing results Achieved by the deep learning of the different discoveries found in this article offer a pipeline to detect anti-personnel pipes Adaptive Neural Networks ( applied to the Ground Penetrating Radar (GPR). The proposed algorithm is suitable to recognize if the scanning format has been received. The acquisition of GPR has a track of anti-personnel pipes. The validity of the said system is made on a real GPR receipt, although systematic training can be done to have relied upon data generated by achievements. Based on the results 95% of the accuracy of detection got achieved without testing acquisition of pipes.
Keywords: GPR, Pipes, CNN, Data Acquisition, B-Scan Image, AUC, ROC.
Scope of the Article: RFID Network and Applications