Projects per year
We report on the development of a computer vision system that analyses video from CCTV systems installed on fishing trawlers for the purpose of monitoring and quantifying discarded fish catch. Our system is designed to operate in spite of the challenging computer vision problem posed by conditions on-board fishing trawlers. We describe the approaches developed for isolating and segmenting individual fish and for species classification. We present an analysis of the variability of manual species identification performed by expert human observers and contrast the performance of our species classifier against this benchmark. We also quantify the effect of the domain gap on the performance of modern deep neural network-based computer vision systems.
|Number of pages||14|
|Journal||ICES Journal of Marine Science|
|Early online date||1 Aug 2019|
|Publication status||Published - Jul 2020|
- Computer vision and CCTV
- Deep learning
- School of Computing Sciences - Associate Professor in Computing Sciences
- Colour and Imaging Lab - Member
Person: Research Group Member, Academic, Teaching & Research
- 1 Finished
Smart fisheries technologies for an efficient, compliant and environmentally friendly fishing sector
Mackiewicz, M., Fisher, M. & French, G.
1/01/18 → 31/12/22