Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification

Langenkämper D, van Kevelaer R, Purser A, Nattkemper TW (2020)
Frontiers in Marine Science 7: 506.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
In marine research, image data sets from the same area but collected at different times allow seafloor fauna communities to be monitored over time. However, ongoing technological developments have led to the use of different imaging systems and deployment strategies. Thus, instances of the same class exhibit slightly shifted visual features in images taken at slightly different locations or with different gear. These shifts are referred to as concept drift in the domains computational image analysis and machine learning as this phenomenon poses particular challenges for these fields. In this paper, we analyse four different data sets from an area in the Peru Basin and show how changes in imaging parameters affect the classification of 12 megafauna morphotypes with a 34-layer ResNet. Images were captured using the ocean floor observation system, a traditional sled-based system, or an autonomous underwater vehicle, which is used as an imaging platform capable of surveying larger regions. ResNet applied on separate individual data sets, i.e., without concept drift, showed that changing object distance was less important than the amount of training data. The results for the image data acquired with the ocean floor observation system showed higher performance values than data collected with the autonomous underwater vehicle. The results from this concept drift studies indicate that collecting image data from many dives with slightly different gear may result in training data well-suited for learning taxonomic classification tasks and that data volume can compensate for light concept drift.
Stichworte
Aquatic Science; Global and Planetary Change; Ocean Engineering; Oceanography; Water Science and Technology
Erscheinungsjahr
2020
Zeitschriftentitel
Frontiers in Marine Science
Band
7
Art.-Nr.
506
eISSN
2296-7745
Page URI
https://pub.uni-bielefeld.de/record/2944758

Zitieren

Langenkämper D, van Kevelaer R, Purser A, Nattkemper TW. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Frontiers in Marine Science. 2020;7: 506.
Langenkämper, D., van Kevelaer, R., Purser, A., & Nattkemper, T. W. (2020). Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Frontiers in Marine Science, 7, 506. doi:10.3389/fmars.2020.00506
Langenkämper, D., van Kevelaer, R., Purser, A., and Nattkemper, T. W. (2020). Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Frontiers in Marine Science 7:506.
Langenkämper, D., et al., 2020. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Frontiers in Marine Science, 7: 506.
D. Langenkämper, et al., “Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification”, Frontiers in Marine Science, vol. 7, 2020, : 506.
Langenkämper, D., van Kevelaer, R., Purser, A., Nattkemper, T.W.: Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Frontiers in Marine Science. 7, : 506 (2020).
Langenkämper, Daniel, van Kevelaer, Robin, Purser, Autun, and Nattkemper, Tim Wilhelm. “Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification”. Frontiers in Marine Science 7 (2020): 506.
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2020-07-20T06:08:56Z
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