About me

Through my interest in technology, I have developed a longstanding interest in robotics, computer science and engineering. My passion for these specific technologies has led me to research in computer vision, novel machine learning and artificial intelligence frameworks that drives innovation in robotic automation.

Prior to joining the University of Lincoln as an MSc student, I participated in a collaboration with Siemens Rail Automation where our team introduced a modern tracking and automated signaling solution experimented with GPS-R powered by the LoRa module to help bring down the fatality rate and cost for the southwest railways.  I also worked as a lab technical assistant in data analyses for a coral research project in the State Key Laboratory of Marine Pollution (SKLMP), City University of Hong Kong. This placement increased my awareness in the ever-increasing importance of computer vision in cutting edge environmental research.

More recently, I worked with AEL(HK) on the CLP critical auxiliary outage project and HKPC Biogas. I challenged myself by shouldering the design of a health and safety monitoring systems utilizing infrared thermography and Cmake’s machine learning face recognition capabilities, that helped in keeping work sites open during the COVID-19 pandemic.

I am currently a PhD candidate at UEA’s world leading Colour and Imaging lab. With this incredible opportunity garner to a wide gamut of experience and knowledge, I wish to be a part of implementing advanced computing technology to the industry.

MSc Project

Sean joined the CDT as a 4-year PHD student as he had already completed the MSc Robotics and Autonomous Systems at the University of Lincoln.

PhD Project

Beyond a shadow of a doubt: land surveying in the real world

Scientific Background 

In our increasingly digitized world, computer vision technology plays a crucial role in a multitude of sectors, from agriculture to coastal monitoring. However, these systems often struggle in environments with variable lighting, particularly when dealing with shadows. This presents a significant challenge in real-world applications, leading to inaccuracies in environment classification and other essential tasks. Our project, in collaboration with the Centre for Environment, Fisheries and Aquaculture Science (CEFAS), seeks to tackle this problem head-on, aiming to develop advanced shadow-invariant processing algorithms that will greatly enhance computer vision capabilities. This timely research aims to bridge the gap between human visual competences and computer vision systems, a pioneering step towards increased accuracy and efficiency in sectors reliant on land and coastal surveying. 

Research Methodology 

The project will follow a comprehensive, hands-on research approach. The student involved will undergo training in measurement and calibration techniques, which will be applied to enhance vision systems used for remotely piloted aircraft. They will work closely with CEFAS to produce an annotated image set, identifying the same material both in and out of shadow regions. The primary focus will be to extend and adapt shadow invariant measurement techniques for the unique challenges posed by farm surveying and coastal monitoring. By incorporating Near Infrared (NIR) technology, the project will explore innovative ways to improve visual perception in shadowy environments. The research will take place both at the lab and in the field, offering a diverse and enriching experience. 

Training 

In addition to gaining experience in cutting-edge computer vision research, the student will develop a robust set of skills in several key areas. They will receive training in the fundamentals of measurement and sensor calibration and get hands-on experience with field deployment of RPA (remotely piloted aircraft) systems. Through the development and implementation of algorithms, they will hone their programming and data analysis skills. They will also be exposed to interdisciplinary collaboration, working closely with professionals from agritech, geophysics, ecology, and computational science sectors. Their scientific research skills will be enhanced through the production of academic papers and contribution to public domain source code. This project offers a unique springboard for a future inter-disciplinary career in the AgriFoRwArdS area, providing a rich blend of theoretical knowledge and practical, industry-relevant experience. 

Join us as we redefine the frontiers of computer vision technology, paving the way for more accurate, efficient, and reliable environmental surveying solutions. Your participation could help shape the future of agricultural and coastal monitoring systems, making a lasting impact on these crucial sectors.