November 2, 2020, by Andrew Edwards (Ed)
Computer Vision and plants – An interview with Bowen Deng
Bowen Deng is a PhD student on the Palaeobenchmarking Resilient Agricultural Systems (PalaeoRAS) project
Why did you decide to do a PhD? What were you doing before?
All my life, I have had a fascination with computing and technology. I have always enjoyed keeping pace with the latest advances in technology and have remained amazed at the speed of computerized developments over the past twenty years. Computer Vision is an enthralling world that can make me feel excited and ambitious. This strongly motivates me.
Prior to being a PhD student at the University of Nottingham, I obtained a bachelor’s degree in Network Engineering from Yunnan University, China, in 2017 and a master’s degree in Advanced Computer Science from the University of Manchester, UK, in 2018.
Why did you choose this particular PhD project?
In this PhD, I can collaborate with many researchers from different schools to explore promising topics and solve valuable problems. I can learn computer science but I can also gain access to interesting knowledge from other areas. I am excited to conduct research in this multi-disciplinary environment.
What are you studying and why is it important?
My current research mainly focuses on deep learning based computer vision technologies, with the project title of ‘Salient Object Detection’. Salient object detection is crucial for the development of computer vision tasks such as visual tracking, robot navigation and automatic image cropping. This can be utilized in biology by, for example, automatically detecting and segmenting the most salient plant in a visual scene.
How do you explain your research to ordinary people?
Salient object detection aims to detect the most distinctive object in an image or a visual scene. Our research is to build efficient and effective salient object detection models. Then, with a multitude of data, we make the model learn how to automatically detect the most important or distinctive object in a visual scene by itself.
How is your first year going? Any highlights or successes?
The first year of my PhD is going well. I have conducted quite interesting research with my supervisors and colleagues and the people in this research group are friendly and welcoming. At this stage of my PhD, we are make promising improvements in the accuracy of deep learning based Salient Object Detection models with competitive performance.
What have you learned in your first PhD year?
During the first year of my PhD, I have learned that focussing on your aims and staying organized is essential. I try to prioritise my main objectives, such as the experiments I need to carry out to reach my goal, and this really helps me work efficiently.
How do you cope with the pressure of doing a PhD?
The University Park campus is an amazing place for releasing the pressure. I often go on a picnic with my friends in the park campus and grabbing a beer after a busy day is always a good choice for me too.
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