Abstract
Edge computing services are contingent on several constraints. There is a requirement needed to provide a proper function, such as low latency, low energy consumption, and high performance. Object detection analysis involves high power resources, it is because of the need to process the images or videos. In this paper, the architecture of edge computing for object recognition is proposed, and the performance of the edge node is examined. The resources performance comparison on Raspberry Pi and Neural Compute Stick are inspected. This study combined the Neural Compute Stick (NCS) to enhance the ability of image processing on Raspberry Pi. Through the aid of NCS, the Raspberry Pi’s frames per second (FPS) is increased by four times when the object detection program is executed, and the energy consumption of the Raspberry Pi is also recorded.
Some of the illustrations in this paper have already been published in: On Construction of Sensors, Edge, and Cloud (iSEC) Framework for Smart System Integration and Applications, in IEEE IoT Journal on 22 June 2020, https://doi.org/10.1109/JIOT.2020.3004244. https://ieeexplore.ieee.org/document/9122603.
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Acknowledgment
This research was supported in part by Ministry of Science and Technology, Taiwan R.O.C., under grants no. 107-2218-E-029-004.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Kristiani, E., Ko, PC., Yang, CT., Huang, CY. (2020). An Edge Computing Architecture for Object Detection. In: Deng, DJ., Pang, AC., Lin, CC. (eds) Wireless Internet. WiCON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 317. Springer, Cham. https://doi.org/10.1007/978-3-030-52988-8_18
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DOI: https://doi.org/10.1007/978-3-030-52988-8_18
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