The following navigation utilizes arrow, enter, escape, and space bar key commands. Left and right arrows move through
main tier links and expand / close menus in sub tiers. Up and Down arrows will open main tier menus and toggle through
sub tier links. Enter and space open menus and escape closes them as well. Tab will move on to the next part of the
site rather than go through menu items.
Student: Jason W. Kollars,
Faculty Mentor: Syed Hassan Ahmed
Department of Computer Science, Georgia Southern University, Statesboro, GA 30460.
Email: email@example.com, firstname.lastname@example.org
The lionfish is a predatory animal that has seen a dramatic increase in population around the world as an invasive species and local marine ecosystems become a threatened due to the lack of natural predators . To combat this issue, unmanned submersibles are currently being developed to autonomously hunt such species. These robots are trained to detect lionfish by Computer Vision and Machine Learning (ML) algorithms . However, we still face latency issues when it comes to the detection of live images in an underwater environment. We are actively studying and analyzing various methodologies to bring robustness to the detection schemes. In this abstract, we are evaluating the existing ML algorithms as well as provide a public Lionfish image dataset for others to train their machines. First, we make a training and testing image dataset of lionfish using an automated search tool. Then we feed the algorithms the training dataset where a trained model is produced. Using the separate testing dataset, we can validate lionfish image recognition. Learning models such as the TensorFlow  ML environment will be used , but others will also be evaluated for image processing along with an analysis for use. The output of our work is expected to show that with an online image dataset, machine learning algorithms can identify a lionfish and also such an approach can benefit other research efforts in ML-based detection and recognition schemes.
 US Department of Commerce, and National Oceanic and Atmospheric Administration. “NOAA National Ocean Service Education: Lionfish Discovery Story.” NOAA's National Ocean Service, 6 Jul 2017.
 Lombardi, Joseph, et al. “Autonomous Lionfish Harvester.” WPI Electronic Projects, 30 April 2018.
 “TensorFlow.” TensorFlow, www.tensorflow.org/ [Last Accessed: 12/4/2018]
 Furlan, Carmelo, and Andrew Boniface. “Senior Project: Lionfish Detection System.” Cal Poly Digital Commons, 15 Jun 2018.
Georgia Southern University