Computer Vision Pipeline for Automated Antarctic Krill Analysis
Not on the map. Why?
Abstract (arXiv)
British Antarctic Survey (BAS) researchers launch annual expeditions to the Antarctic in order to estimate Antarctic Krill biomass and assess the change from previous years. These comparisons provide insight into the effects of the current environment on this key component of the marine food chain. In this work we have developed tools for automating the data collection and analysis process, using web-based image annotation tools and deep learning image classification and regression models. We achieve highly accurate krill instance segmentation results with an average 77.28% AP score, as well as separate maturity stage and length estimation of krill specimens with 62.99% accuracy and a 1.98mm length error respectively.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 12 Sep 2023.
Builds on
8 of 24 references.
References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2019Object Detection in 20 Years: A Surveycited by 531
- 2018Object Detection with Deep Learning: A Reviewcited by 528
- 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
- 2019The VIA Annotation Software for Images, Audio and Videocited by 167
- 2020BoxInst: High-Performance Instance Segmentation with Box Annotationscited by 151
Built on by
Not cited yet.