Jellyfish Identification Software for Underwater Laser Cameras (JTRACK)

Abstract

Jellyfish can form erratic blooms in response to seasonal and irregular changes in environmental conditions with often large, transient effects on local ecosystem structure as well as effects on several sectors of the marine and maritime economy. Early warning systems able to detect conditions for jelly fish proliferation can enable management responses to mitigate such effects providing benefit to local ecosystems and economies. We propose here the creation of a research team in response to the EU call for proposal under the European Maritime and Fisheries Fund called “Blue Labs: innovative solutions for maritime challenges”. The project will establish a BLUELAB team with a strong cross-sectorial component that will benefit of the expertise of researchers in IT and Marine Biology, Computer Vision and embedded systems, which will work in collaboration with Industry and Policy maker to develop an early warning system using a new underwater imaging system based on Time of Flight Laser cameras. The camera will be combined to machine learning algorithm allowing autonomous early detection of jellyfish species (e.g. polyp, ephyra and planula stages). The team will develop the system and the companion software and will demonstrate its applications in real case conditions.

Keywords

• Machine Learning
• Lidar
• Species Identification