Object detection in aerial images has been an active research area thanks to the vast availability of unmanned aerial vehicles (UAVs). Along with the increase of computational power, deep learning algorithms are commonly used for object detection tasks. However, aerial images have large variations, and the object sizes are usually small, rendering lower detection accuracy. Besides, real-time inferencing on low-cost edge devices remains an open-ended question. In this work, we explored the usage of state-of-the-art deep learning object detection on low-cost edge hardware. We propose YOLO-RTUAV, an improved version of YOLOv4-Tiny, as the solution. We benchmarked our proposed models with various state-of-the-art models on the VAID and COWC datasets. Our proposed model can achieve higher mean average precision (mAP) and frames per second (FPS) than other state-of-the-art tiny YOLO models, especially on a low-cost edge device such as the Jetson Nano 2 GB. It was observed that the Jetson Nano 2 GB can achieve up to 12.8 FPS with a model size of only 5.5 MB.

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People and things become mobile sensors that converge to our daily life. This has unwittingly collected humongous of time series of data with location. People are finding ways to turn this raw data into valuable information as a distinguished business analytic. Importantly, the demand of speedy computation with an appealing visualization is crucial to success. Thus, it reveals the potential economic benefits and becomes an overwhelming new research area that requiring sophisticated mechanisms and technologies to reach the demand. Over the past decade, there have attempts of using accelerators along with multicore CPUs in boosting large-scale data computation. We proposed an emerging SQL-like GPU query accelerator, GalacticaDB. In addition, we extended it to have the geo-spatial compute capabilities. The query operation executes parallelly with drawing support from a high performance and energy efficient NVIDIA Tesla technology. Our result has shown the significant speedup by using GalacticaDB.

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