UNDERGROUND CRUDE OIL PIPELINE LEAKAGE DETECTION USING DEXINED DEEP LEARNING TECHNIQUES AND LAB COLOR SPACE

Abstract

Computer vision plays a big role in pipeline leakage detection systems and is one of the latest techniques. Still, it requires a powerful image-processing algorithm to detect objects. The purpose of this work is to develop and implement spill detection in oil pipes caused by leakage using images taken by a drone equipped with a Raspberry Pi 4. The acquired images are sent to the base station along with the global positioning system (GPS) location of the captured images via the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, images are processed to identify contours by dense extreme inception networks for edge detection(DexiNed) deep learning techniques based on holistically-nested edge detection(HED) and extreme inception (Xception) networks. This algorithm is capable of finding many contours in images. To find a contour with black color, the CIELAB color space (LAB) has been used. The proposed algorithm removes small contours and computes the area of the remaining contours. If the contour is above the threshold value, it is considered a spill; otherwise, it will be saved in a database for further inspection. For testing purposes, three different spill areas were implemented with spill sizes of (1 m^2,2 m^2 ,and 3 m^2). Images have been captured at three different heights (5 m, 10 m, and 15 m) by the drone used to capture the images. The result shows that effective detection has been obtained at 10 meters high. To monitor the entire system, a web application has been integrated into the base station.