Journal article
Automatic detection of Western rock lobster using synthetic data
ICES Journal of Marine Science, Vol.77(4), pp.1308-1317
2019
Abstract
Underwater imaging is being extensively used for monitoring the abundance of lobster species and their biodiversity in their local habitats. However, manual assessment of these images requires a huge amount of human effort. In this article, we propose to automate the process of lobster detection using a deep learning technique. A major obstacle in deploying such an automatic framework for the localization of lobsters in diverse environments is the lack of large annotated training datasets. Generating synthetic datasets to train these object detection models has become a popular approach. However, the current synthetic data generation frameworks rely on automatic segmentation of objects of interest, which becomes difficult when the objects have a complex shape, such as lobster. To overcome this limitation, we propose an approach to synthetically generate parts of the lobster. To handle the variability of real-world images, these parts were inserted into a set of diverse background marine images to generate a large synthetic dataset. A state-of-the-art object detector was trained using this synthetic parts dataset and tested on the challenging task of Western rock lobster detection in West Australian seas. To the best of our knowledge, this is the first automatic lobster detection technique for partially visible and occluded lobsters.
Details
- Title
- Automatic detection of Western rock lobster using synthetic data
- Authors/Creators
- A. Mahmood (Author/Creator)M. Bennamoun (Author/Creator)S. An (Author/Creator)F. Sohel (Author/Creator)F. Boussaid (Author/Creator)R. Hovey (Author/Creator)G. Kendrick (Author/Creator)C. Beyan (Author/Creator)
- Publication Details
- ICES Journal of Marine Science, Vol.77(4), pp.1308-1317
- Publisher
- Oxford University Press
- Identifiers
- 991005544425307891
- Copyright
- © 2019 International Council for the Exploration of the Sea
- Murdoch Affiliation
- College of Arts, Business, Law and Social Sciences
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
- Web Of Science research areas
- Fisheries
- Marine & Freshwater Biology
- Oceanography
- ESI research areas
- Plant & Animal Science