Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools
Author:
Belcher Byron T., Bower Eliana H., Burford Benjamin, Celis Maria Rosa, Fahimipour Ashkaan K.ORCID, Guevara Isabella L., Katija KakaniORCID, Khokhar Zulekha, Manjunath Anjana, Nelson Samuel, Olivetti SimoneORCID, Orenstein EricORCID, Saleh Mohamad H., Vaca Brayan, Valladares Salma, Hein Stella A., Hein Andrew M.ORCID
Abstract
ABSTRACTImage-based machine learning methods are quickly becoming among the most widely-used forms of data analysis across science, technology, and engineering. These methods are powerful because they can rapidly and automatically extract rich contextual and spatial information from images, a process that has historically required a large amount of manual labor. The potential of image-based machine learning methods to change how researchers study the ocean has been demonstrated through a diverse range of recent applications. However, despite their promise, machine learning tools are still under-exploited in many domains including species and environmental monitoring, biodiversity surveys, fisheries abundance and size estimation, rare event and species detection, the study of wild animal behavior, and citizen science. Our objective in this article is to provide an approachable, application-oriented guide to help researchers apply image-based machine learning methods effectively to their own research problems. Using a case study, we describe how to prepare data, train and deploy models, and avoid common pitfalls that can cause models to underperform. Importantly, we discuss how to diagnose problems that can cause poor model performance on new imagery to build robust tools that can vastly accelerate data acquisition in the marine realm. Code to perform our analyses is provided athttps://github.com/heinsense2/AIO_CaseStudy
Publisher
Cold Spring Harbor Laboratory
Reference102 articles.
1. Abadi, M. , Barham, P. , Chen, J. , Chen, Z. , Davis, A. , Dean, J. , Devin, M. , Ghemawat, S. , Irving, G. , Isard, M. and Kudlur, M. , 2016. {TensorFlow}: a system for {Large-Scale} machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 2. Fish species identification using a convolutional neural network trained on synthetic data 3. Risk attitudes and human mobility during the COVID-19 pandemic 4. Deep learning-based appearance features extraction for automated carp species identification;Aquacultural Engineering,2020 5. The iwildcam 2021 competition dataset;arXiv preprint,2021
|
|