Abstract
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 twelve 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 a modern 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.
Highlights
Recent developments in machine learning-based classification and object detection in computer vision has been greatly influenced by deep learning algorithms (LeCun et al, 2015)
Concept drifts can have a significant negative influence on the performance of machine learning classifiers that are trained on one data set, and re-applied to new “unseen” data, where the performance of the classifier decreases for this new data
As changes in gear and operation for many studies cannot be avoided, the question as to what extent marine imaging can benefit from computer vision research depends on the ability of computer vision systems to compensate for the effects of such concept drifts
Summary
Recent developments in machine learning-based classification and object detection in computer vision has been greatly influenced by deep learning algorithms (LeCun et al, 2015). First attempts to link these two developments have been successful and showed the potential of deep learning in e.g., morphotype detection (Zurowietz et al, 2018), morphotype classification (Smith and Dunbabin, 2007; Gobi, 2010; Beijbom et al, 2012; Bewley et al, 2012; Kavasidis and Palazzo, 2012; Schoening et al, 2012; Langenkämper et al, 2018, 2019; Mahmood et al, 2019; Piechaud et al, 2019) or polyp behavior monitoring (Osterloff et al, 2019) All these studies have reported results obtained for data sets collected with the same gear, i.e., with one distinct camera system and the platform for the full analyzed data set. As changes in gear and operation for many studies cannot be avoided, the question as to what extent marine imaging can benefit from computer vision research depends on the ability of computer vision systems to compensate for the effects of such concept drifts
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
