RGBThermal Fusion Network for Leakage Detection of Crude Oil Transmission Pipes Detection and diagnosis of defects in pipelines that transmit crude oil is crucial for assuring the safety and integrity of crude oil transportation. With the development of deep learning technologies, studies have been done in the application of neural networks to the defect detection of crude oil transmission pipeline. However, most of the imagebased detection research use RGB images from optical cameras to make decisions, and their outcomes are easily influenced by poor lighting conditions. Further more, RGB images provide only an external view of the pipe while the pipeline defects can be within the structure. In order to obtain a high accuracy and robust classification for the leakage level in crude oil transmission pipelines, the present paper takes advantages of thermal images and fuses them with RGB images using a novel convolutional network (CNN). Specifically, the paper proposes an intelligent network architecturebased CNN to which the features of RGB images and thermal images are integrated. The experimental results verify that the proposed method provides outstanding performance in the leakage level classification for crude oil pipe.

Realtime leak detection using an infrared camera and Faster RCNN technique Abstract   Realtime hydrocarbon leak detection is an essential part of process safety and loss prevention program. Optical gas imaging (OGI) is one of the attractive methods to monitor hydrocarbon leak in the processing system. The manual analysis of a video frame to detect a potential leak is cumbersome and errorprone. The purpose of this study is to develop the automated hydrocarbon leak detection using appropriate technology and numerical technique. This is achieved by integrating Faster RegionConvolutional Neural Network (Faster RCNN) technique with the OGI technology. The application of the procedure is demonstrated using the videos of the hydrocarbon leaks from an Ethane cracker plant. The videos are used to train the Faster RCNN and subsequently used for the testing. The performance of the proposed integrated approach is compared with the Single Shot MultiBox Detector (SSD) models. The results confirm the proposed optimal model is superior compared to the SSD models.