Date of Award

Summer 2024

Access Type

Thesis - Open Access

Degree Name

Master of Science in Mechanical Engineering

Department

Mechanical Engineering

Committee Chair

Monica Garcia

First Committee Member

Bryan Watson

Second Committee Member

Christopher Hockley

College Dean

James W. Gregory

Abstract

Neural networks have been used for object detection and recognition in both color and intensity camera images. As the use of infrared cameras, colloquially termed thermal cameras, has increased and costs have decreased, object detection and recognition in infrared camera images have been increasingly studied. An infrared image is treated as an intensity image, just like a grayscale camera image, except the intensity corresponds to infrared radiation instead of visible light. The information provided by these two types of images are different, especially in different lighting and environmental situations, and some types of objects are more easily recognized in visible light images, and other types are more easily recognized in infrared images. The fusion of information from both types of images can lead to better object recognition outcomes. Here, several fusion methods of infrared images and visible light images are examined, including a Hue Saturation Value-based fusion method that has not been evaluated in effectiveness in object recognition. This study evaluates the feasibility of recognizing objects with these fused visible light-infrared images across autonomous systems, including commercial, military and law enforcement applications.

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