This thesis presents the developments of a vision-based system for
aerial pipeline Right-of-Way surveillance using optical/Infrared sensors mounted
on Unmanned Aerial Vehicles (UAV). The aim of research is to develop a highly
automated, on-board system for detecting and following the pipelines; while
simultaneously detecting any third-party interference. The proposed approach
of using a UAV platform could potentially reduce the cost of monitoring and
surveying pipelines when compared to manned aircraft. The main contributions
of this thesis are the development of the image-analysis algorithms, the overall
system architecture and validation of in hardware based on scaled down Test
environment.
To evaluate the performance of the system, the algorithms were coded using
Python programming language. A small-scale test-rig of the pipeline structure,
as well as expected third-party interference, was setup to simulate the
operational environment and capture/record data for the algorithm testing and
validation.
The pipeline endpoints are identified by transforming the 16-bits depth data of
the explored environment into 3D point clouds world coordinates. Then, using
the Random Sample Consensus (RANSAC) approach, the foreground and
background are separated based on the transformed 3D point cloud to extract
the plane that corresponds to the ground. Simultaneously, the boundaries of the
explored environment are detected based on the 16-bit depth data using a
canny detector. Following that, these boundaries were filtered out, after being
transformed into a 3D point cloud, based on the real height of the pipeline for fast and accurate measurements using a Euclidean distance of each boundary
point, relative to the plane of the ground extracted previously. The filtered
boundaries were used to detect the straight lines of the object boundary (Hough
lines), once transformed into 16-bit depth data, using a Hough transform
method. The pipeline is verified by estimating a centre line segment, using a 3D
point cloud of each pair of the Hough line segments, (transformed into 3D).
Then, the corresponding linearity of the pipeline points cloud is filtered within
the width of the pipeline using Euclidean distance in the foreground point cloud.
Then, the segment length of the detected centre line is enhanced to match the
exact pipeline segment by extending it along the filtered point cloud of the
pipeline.
The third-party interference is detected based on four parameters, namely:
foreground depth data; pipeline depth data; pipeline endpoints location in the
3D point cloud; and Right-of-Way distance. The techniques include detection,
classification, and localization algorithms.
Finally, a waypoints-based navigation system was implemented for the air-
vehicle to fly over the course waypoints that were generated online by a
heading angle demand to follow the pipeline structure in real-time based on the
online identification of the pipeline endpoints relative to a camera frame
