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Introduction In recent years, the field of autonomous drone navigation has experienced significant advancements, particularly with the integration of low-latency Neural Processing Units (NPUs). These architectures are essential for real-time image recognition, enabling drones to navigate complex environments efficiently. This blog post evaluates the latest low-latency NPU architectures and their impact on image recognition capabilities

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Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today’s object detection technique as a revolution driven by deep learning, then, back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This article extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century’s time (from the 1990s to 2022). A number of topics have been covered in this article, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speedup techniques, and recent state-of-the-art detection methods.

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