Computer Vision Assisted Deep Learning Enabled Gas Pipeline Leak Detection Framework
Suri Babu Nuthalapati, M. Arun, C. Prajitha, S Rinesh, K M Abubeker
Abstract
Safe and economical infrastructure facilitates gas and oil transportation via pipeline networks. Leaks in natural gas pipelines, caused by ageing and other reasons, are common yet difficult to identify. Nevertheless, the most prevalent issue that impacts pipeline operation is leakage failure. To a large extent, the flow properties of the particular pipeline determine the accuracy with which a leaking location may be located. One helpful method for analyzing the many assumptions used to explain the fluid flow process and parameters is numerical modelling based on Artificial Intelligence (AI). Hence, this r4search suggests the Deep Learningassisted Gas Pipeline Leakage Detection System (DLGPLDS) using infrared cameras to accurately detect the leak location. A system of infrared cameras is suggested for use in gas plants, transportation, and manufacturing to track the undetectable leaks of methane gas in real-time. This research recommends using a histogram equalization procedure to remove the background noise effectively. The leaking of invisible gas may be successfully identified by this knowledge-based infrared camera system. This novel approach uses CNNs to categorize infrared camera images into groups based on whether or not they contain natural gas leaks. The research findings demonstrate that the suggested DLGPLDS model increases the gas leakage prediction ratio by 98.7%, classification accuracy ratio by $\mathbf{9 7. 4 \%}$, PSNR ratio by 96.5%, and error rate by 10.3% compared to other state-of-art models.