Sensing, Robotic/Computer Vision
- Perrett, A., Pollard, H., Schofield, M., Qie, L., Bosilj, P., & Brown, J. (2023) DeepVerge: Classification of roadside verge biodiversity and conservation potential. Computers, Environment and Urban Systems, 102(2023), pp. 101968.
- Ravikanna, R., Heselden, J., Arshad Khan, M., Perrett, A., Zhu, Z., Das, G. Hanheide, M. (2023) Smart Parking System Using Heuristic Optimization for Autonomous Transportation Robots in Agriculture. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 38-50. Springer, Cham.
I have a mixed background in electronic engineering and digital electronic fault finding within phototypesetters and industrial control gear. Programming of embedded devices and writing component level (sensor) device drivers has linked my interests within digital electronics and software throughout my life. I have also owned and ran a small Internet Service Provider which linked interests in digital communication and business. Ten years prior to arriving at the University of Lincoln to study a BSc in Computer Science, and then a MSc by Research, I had a “back to basics” semi self-sufficient lifestyle. This saw me becoming a very small-scale market gardener and selling home grown fruit and veg produce on a market stall (small scale farming). The AgriFoRwArdS CDT was a logical step to not only bring many of my interests together but take them to a much higher level. I have continued with the University of Lincoln purely because both the staff and Agri-Robotic opportunities are fantastic.
Addressing geographical domain shift for quantification of UK road verge biodiversity
Previous work on DeepVerge demonstrated a method to survey biodiversity levels of roadside verges within one geographic locale using a convolutional neural network and a fully connected neural network (FCNN) classifier . Known limitations were left as future work, which now require addressing. Ordinal regression may exploit naturally occurring ranking information contained within the survey ground truth data unavailable to DeepVerge’s nominal classification method, allowing the problem of domain shift to then be addressed effectively. Addressing domain shift will extend this work providing DeepVerge with a methodology to expand beyond one locale and survey biodiversity at a national level.
To be confirmed