Rice plants are susceptibility to pest infestations and various diseases. Convolutional neutral networks (CNNs) have greatly improved accuracy and speed for rice diseases. However, important textures are lost with the...
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Since their prevalence in World War II, Radar-based systems have provided a strategic advantage for military applications. Since then, radars have merged into everyday commercial products ranging from Automotive senso...
详细信息
ISBN:
(数字)9781665492997
ISBN:
(纸本)9781665492997
Since their prevalence in World War II, Radar-based systems have provided a strategic advantage for military applications. Since then, radars have merged into everyday commercial products ranging from Automotive sensors for adaptive cruise control, to home security systems used to protect one's home. Due to their various use cases for pattern recognition, classification, and Computer Vision tasks, many radar-systems incorporate machine learning models. This paper aims to implement a real-time tracking system comprised of a low-cost transceiver and Computer Vision model. To determine the most optimal setup, the study will compare implementations that include two low-cost transceivers and two different weights from the YOLOv3 algorithm. The comparison will determine the most optimal constraints for the tracking system by measuring system latency, and classification confidence.
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