Oughton, Edward2023-06-192023-06-19https://hdl.handle.net/1920/13373Object detection models commonly focus on utilizing the visible spectrum via Red-Green-Blue (RGB) imagery. Due to various limitations with this approach in low visibility settings, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) (7.5 - 13.5 μm) images to increase object detection performance. However, we still lack baseline performance metrics evaluating RGB, LWIR and RGB-LWIR fused object detection machine learning models, especially from air-based platforms. This study undertakes such an evaluation finding that a blended RGB-LWIR model generally exhibits superior performance compared to traditional RGB or LWIR approaches. For example, an RGB-LWIR blend only performed 1-5% behind the RGB approach in predictive power across various altitudes and periods of clear visibility. Yet, RGB fusion with a thermal signature overlayed provides edge redundancy and edge emphasis, both which are vital in supporting edge detection machine learning algorithms.masters thesesenCopyright 2023 James GallagherTextThermal object detectionLong Wave Infrared (LWIR)Uncrewed Arial Systems (UAS)Machine learning