Engineering Commons^™

Deep Semantic Hashing For Aerial Livestock Detection, Shosei Anegawa, Franz Kurfess, Sumona Mukhopadhyay

College of Engineering Summer Undergraduate Research Program

The goal of this project is to be able to accurately detect and count livestock in footage captured by a drone in real time. The main problems with this arise from the fact that a drone can only carry limited computing resources, and hashing is conventionally thought of as a great method of doing image classification very quickly and thus even on low-power devices. In this project, we use both a Faster-RCNN, which is a state-of-the art object detection model as a benchmark to develop a hashing model that can perform a similar task much more quickly. These two models …