Liquid Leak Detection Using Thermal Images
Kalpak Bansod Yanshan Wan Yugesh Rai
This paper presents a comprehensive solution to address the critical
challenge of liquid leaks in the oil and gas industry, leveraging advanced
computer vision and deep learning methodologies. Employing You Only Look Once
(YOLO) and Real-Time Detection Transformer (RT DETR) models, our project
focuses on enhancing early identification of liquid leaks in key infrastructure
components such as pipelines, pumps, and tanks. Through the integration of
surveillance thermal cameras and sensors, the combined YOLO and RT DETR models
demonstrate remarkable efficacy in the continuous monitoring and analysis of
visual data within oil and gas facilities. YOLO's real-time object detection
capabilities swiftly recognize leaks and their patterns, while RT DETR excels
in discerning specific leak-related features, particularly in thermal images.
This approach significantly improves the accuracy and speed of leak detection,
ultimately mitigating environmental and financial risks associated with liquid
leaks.