Field Programmable Gate Array (FPGA) Based Fish Detection Using Haar Classifiers

Abstract

The quantification of abundance, size, and distribution of fish is critical to properly manage and protect marine ecosystems and regulate marine fisheries. Currently, fish surveys are conducted using fish tagging, scientific diving, and/or capture and release methods (i.e., net trawls), methods that are both costly and time consuming. Therefore, providing an automated way to conduct fish surveys could provide a real benefit to marine managers. In order to provide automated fish counts and classification we propose an automated fish species classification system using computer vision. This computer vision system can count and classify fish found in underwater video images using a classification method known as Haar classification. We have partnered with the Birch Aquarium to obtain underwater images of a variety of fish species, and present in this paper the implementation of our vision system and its detection results for our first test species, the Scythe Butterfly fish, subject of the Birch Aquarium logo.