This paper investigates the problem of object detection
for real-time agents’ navigation using embedded systems. In realworld problems, a compromise between accuracy and speed must be found. In this paper, we consider a description of the architecture of different object detection algorithms, such as R-CNN and YOLO, to compare them on different variants of embedded systems using different datasets. As a result, we provide a trade-off study based on accuracy and speed for different object detection algorithms to choose the appropriate one depending on the specific application task.

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