Monitoring of the marine environment requires large amounts of data, simply due to its vast size. Therefore, underwater autonomous vehicles and drones are increasingly deployed to acquire numerous photographs. However, ecological conclusions from them are lagging as the data requires expert annotation and thus realistically cannot be manually processed. This calls for developing automatic classification algorithms dedicated for this type of data. Current out-of-the-box solutions struggle to provide optimal results in these scenarios as the marine data is very different from everyday data. Images taken under water display low contrast levels and reduced visibility range thus making objects harder to localize and classify. Scale varies dramatically because of the complex 3 dimensionality of the scenes. In addition, the scarcity of labeled marine data prevents training these dedicated networks from scratch. In this work, we demonstrate how transfer learning can be utilized to achieve high quality results for both detection and classification in the marine environment. We also demonstrate tracking in videos that enables counting and measuring the organisms. We demonstrate the suggested method on two very different marine datasets, an aerial dataset and an underwater one.
|Title of host publication||Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|State||Published - 13 Dec 2018|
|Event||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States|
Duration: 18 Jun 2018 → 22 Jun 2018
|Name||IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops|
|Conference||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018|
|City||Salt Lake City|
|Period||18/06/18 → 22/06/18|
Bibliographical noteFunding Information:
This work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 773753 (SYMBIOSIS), the Leona M. and Harry B. Helmsley Charitable Trust, the Maurice Hatter Foundation, and the Technion Ollendorff Minerva Center for Vision and Image Sciences. Research at the University of Haifa was conducted at the Hatter Department for Marine Technologies and the Morris Kahn Marine Research Station, Department of Marine Biology.
© 2018 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering