College - Author 1

College of Engineering

Department - Author 1

Computer Engineering Department

Degree Name - Author 1

BS in Computer Engineering

College - Author 2

College of Engineering

Department - Author 2

Computer Engineering Department

Degree - Author 2

BS in Computer Engineering



Primary Advisor

Andrew Danowitz, College of Engineering, Electrical Engineering Department


Deep neural networks have proven to be an effective method in classification of images. The ability to recognize objects has opened the door for many new systems which use image classification to solve challenging problems where conventional image classification would be inadequate. We trained a large, deep convolutional neural network to identify lionfish from other species that might be found in the same habitats. Google’s Inception framework served as a powerful platform for our fish recognition system. By using transfer learning, we were able to obtain exceptional results for the classification of different species of fish. The convolutional neural network was then moved to a Raspberry Pi system enclosed in a water proof case that allowed for the convolutional neural network to be run on underwater images taken by the system.