Dong, X., & Dukes, X. A., & Littleton, J., & Neville, T., & Rollerson, C., & Quinney, A. L. (2022, March), Object Detection on Raspberry Pi  Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas. 10.18260/1-2--39192

\bibitem{asee_peer_39192}
  Dong, X., \& Dukes, X. A., \& Littleton, J., \& Neville, T., \& Rollerson, C., \& Quinney, A. L. (2022, March), \emph{Object Detection on Raspberry Pi}
  Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas.
  10.18260/1-2--39192

Xishuang Dong, Xavier Alexander Dukes, Joshua Littleton, Tri'Heem Neville, Christopher Rollerson, and Arthur L Quinney.  "Object Detection on Raspberry Pi".  2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas, 2022, March.  ASEE Conferences, 2022.  https://peer.asee.org/39192 Internet.  20 Jan, 2025

\bibitem{asee_peer_39192}
  Xishuang Dong, Xavier Alexander Dukes, Joshua Littleton, Tri'Heem Neville, Christopher Rollerson, and Arthur L Quinney.
  "Object Detection on Raspberry Pi".
  \emph{2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas, 2022, March}.
  ASEE Conferences, 2022.
  https://peer.asee.org/39192 Internet.  20 Jan, 2025

@INPROCEEDINGS{asee_peer_39192
author = "Xishuang Dong, Xavier Alexander Dukes, Joshua Littleton, Tri'Heem Neville, Christopher Rollerson, and Arthur L Quinney"
title = "Object Detection on Raspberry Pi"
booktitle = "2022 ASEE Gulf Southwest Annual Conference"
year = "2022"
month = "March"
address = "Prairie View, Texas"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/39192}
number = {10.18260/1-2--39192}
}

TY  - CPAPER
AB  - Internet of Things (IoT) refers to the billions of edge devices around the world connected to the internet for various applications such as collecting and analyzing data. Data analytics on these edge devices can protect user privacy and reduce communication costs for these applications. Specifically, computer vision is a core technique to build these applications on these devices, where object detection is an imperative task that is to recognize the category of the object and label its location. This paper proposed to implement object detection on Raspberry Pi via machine learning models. It is to employ Raspberry Pi Kits and web camera to detect predefined objects by running mobile deep learning models. Moreover, it will extend existing models to recognize weapons in images with transfer learning techniques, which is to fine-tune these models on new image datasets collected from Internet. In addition, it will apply Google accelerator to improve the detection speed. Preliminary work has shown that the models can detect general objects such as person, keyboard, laptop, and table on Raspberry Pi 3 effectively.
AU  - Xishuang Dong
AU  - Xavier Alexander Dukes
AU  - Joshua Littleton
AU  - Tri'Heem Neville
AU  - Christopher Rollerson
AU  - Arthur L Quinney
CY  - Prairie View, Texas
DA  - 2022/03/16
PB  - ASEE Conferences
TI  - Object Detection on Raspberry Pi
UR  - https://peer.asee.org/39192
DO  - 10.18260/1-2--39192
ER  -