A new method to control error rates in automated species identification with deep learning algorithms

Villon, Sebastien, Mouillot, David, Chaumont, Marc, Subsol, Gerard, Claverie, Thomas, and Villeger, Sebastien (2020) A new method to control error rates in automated species identification with deep learning algorithms. Scientific Reports, 10. 10972.

PDF (Published Version) - Published Version
Available under License Creative Commons Attribution.

Download (4MB) | Preview
View at Publisher Website: http://dx.doi.org/10.1038/s41598-020-675...


Processing data from surveys using photos or videos remains a major bottleneck in ecology. Deep Learning Algorithms (DLAs) have been increasingly used to automatically identify organisms on images. However, despite recent advances, it remains difficult to control the error rate of such methods. Here, we proposed a new framework to control the error rate of DLAs. More precisely, for each species, a confidence threshold was automatically computed using a training dataset independent from the one used to train the DLAs. These species-specific thresholds were then used to post-process the outputs of the DLAs, assigning classification scores to each class for a given image including a new class called "unsure". We applied this framework to a study case identifying 20 fish species from 13,232 underwater images on coral reefs. The overall rate of species misclassification decreased from 22% with the raw DLAs to 2.98% after post-processing using the thresholds defined to minimize the risk of misclassification. This new framework has the potential to unclog the bottleneck of information extraction from massive digital data while ensuring a high level of accuracy in biodiversity assessment.

Item ID: 63897
Item Type: Article (Research - C1)
ISSN: 2045-2322
Related URLs:
Copyright Information: © The Author(s) 2020. Open Access Tis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Additional Information:

An amendment to this paper was published 04 September 2020. Please see Related URLs.

Date Deposited: 29 Jul 2020 07:35
FoR Codes: 31 BIOLOGICAL SCIENCES > 3103 Ecology > 310305 Marine and estuarine ecology (incl. marine ichthyology) @ 100%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 100%
Downloads: Total: 788
Last 12 Months: 18
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page