Application of YOLOv5 for mask detection on IoT

Research Article
Open access

Application of YOLOv5 for mask detection on IoT

Cheng Huang 1* , Yishen Liu 2 , Jiahao Li 3 , Hao Tian 4 , Haoyi Chen 5
  • 1 The Chinese University of Hong Kong    
  • 2 Perking University    
  • 3 The Chinese University of Hong Kong    
  • 4 The Hong Kong Polytechnic University    
  • 5 The Chinese University of Hong Kong    
  • *corresponding author chenghuang@link.cuhk.edu.hk
Published on 26 December 2023 | https://doi.org/10.54254/2755-2721/29/20230708
ACE Vol.29
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-259-6
ISBN (Online): 978-1-83558-260-2

Abstract

The combination of the Internet of Things and deep learning technology is usually accompanied by many problems, such as limited bandwidth and computing resources. IoT combined with deep learning often causes system freezes or delays due to limited computing resources. Upgrading the hardware equipment of the IoT system requires a large economic cost, but using a lightweight deep learning model can reduce the consumption of hardware resources to adapt to the actual scene. In this paper, we combine IoT technology and improve a lightweight deep learning model, YOLOv5, to assist people in mask detection, vehicle counting, and target tracking, which does not take up too many computing resources. We deployed the improved YOLOv5 on the server side, and completed the training in the container. The weight file after training was deployed in Docker, and then combined with Kubernates to get the final experimental results. The resulting graph can be displayed by opening a browser at the edge node and entering the relevant IP address. Users can also perform certain operations on the results in the front end of the browser, such as drawing a horizontal line in the road to complete the local vehicle count. These operations are also fed back to the server for interaction with developers. For improved YOLOv5, the recognition speed and accuracy are faster than before. At the same time, compared with the previous version, the model itself requires less storage space and is easier to deploy, making the model easier to implement in the operation of edge nodes. Theoretical analysis and experimental results verify the feasibility and superiority of the proposed method.

Keywords:

Internet of Things, deep learning, YOLOv5, object detection

Huang,C.;Liu,Y.;Li,J.;Tian,H.;Chen,H. (2023). Application of YOLOv5 for mask detection on IoT. Applied and Computational Engineering,29,1-11.
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References

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[3]. J. Hwang, L. Nkenyereye, N. Sung, J. Kim and J. Song, ”IoT Service Slicing and Task Offloading for Edge Computing,” in IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11526-11547, 15 July15, 2021.

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[11]. C. Fan, J. Yi, J. Tao, Z. Tian, B. Liu and Z. Wen, ”Gated Recurrent Fusion With Joint Training Framework for Robust End-to-End Speech Recognition,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 198-209, 2021.

[12]. L. Chai, J. Du, Q. -F. Liu and C. -H. Lee, ”A Cross-Entropy-Guided Measure (CEGM) for Assessing Speech Recognition Performance and Optimizing DNN-Based Speech Enhancement,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 106-117, 2021.

[13]. Z. Zhang, B. Zhong, S. Zhang, Z. Tang, X. Liu and Z. Zhang, ”Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1024-1033.

[14]. N. Wang, W. Zhou, J. Wang and H. Li, ”Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1571-1580.

[15]. YOLOv5, https://github.com/ultralytics/yolov5.

[16]. M. Tan, R. Pang and Q. V. Le, ”EfficientDet: Scalable and Efficient Object Detection,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 10778-10787.

[17]. K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu and C. Xu, ”GhostNet: More Features From Cheap Operations,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1577- 1586.

[18]. Kubemetes, https://kubernetes.io/.

[19]. L. -C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, ”DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-848, 1 April 2018.

[20]. L. Sifre and S. Mallat, ”Rigid-motion scattering for texture classification”, arXiv:1403.1687 [cs], Mar. 2014.

[21]. A. Howard et al., ”Searching for MobileNetV3,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314- 1324.

[22]. S. Liu, L. Qi, H. Qin, J. Shi and J. Jia, ”Path Aggregation Network for Instance Segmentation,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 8759-8768.

[23]. T. -Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan and S. Belongie, ´ ”Feature Pyramid Networks for Object Detection,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.

[24]. Real-World Masked Face Dataset, https://github.com/Xzhangyang/Real-World-Masked-Face-Dataset.

Cite this article

Huang,C.;Liu,Y.;Li,J.;Tian,H.;Chen,H. (2023). Application of YOLOv5 for mask detection on IoT. Applied and Computational Engineering,29,1-11.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-259-6(Print) / 978-1-83558-260-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.29
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. L. Chettri and R. Bera, ”A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems,” in IEEE Internet of Things Journal, vol. 7, no. 1, pp. 16-32, Jan. 2020.

[2]. G. Xu et al., ”TT-SVD: An Efficient Sparse Decision-Making Model With Two-Way Trust Recommendation in the AI-Enabled IoT Systems,” in IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9559-9567, 15 June15, 2021.

[3]. J. Hwang, L. Nkenyereye, N. Sung, J. Kim and J. Song, ”IoT Service Slicing and Task Offloading for Edge Computing,” in IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11526-11547, 15 July15, 2021.

[4]. F. Hussain, R. Hussain, S. A. Hassan and E. Hossain, ”Machine Learning in IoT Security: Current Solutions and Future Challenges,” in IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686-1721, thirdquarter 2020.

[5]. B. Yuan, J. Wang, P. Wu and X. Qing, ”IoT Malware Classification Based on Lightweight Convolutional Neural Networks,” in IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3770-3783, 1 March1, 2022.

[6]. B. Mao, Y. Kawamoto and N. Kato, ”AI-Based Joint Optimization of QoS and Security for 6G Energy Harvesting Internet of Things,” in IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7032-7042, Aug. 2020.

[7]. R. He, J. Cao, L. Song, Z. Sun and T. Tan, ”Adversarial Cross-Spectral Face Completion for NIR-VIS Face Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 5, pp. 1025- 1037, 1 May 2020.

[8]. C. Fu, X. Wu, Y. Hu, H. Huang and R. He, ”DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 2938- 2952, 1 June 2022.

[9]. K. He, G. Gkioxari, P. Dollar and R. Girshick, ”Mask R-CNN,” in IEEE ´ Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 386-397, 1 Feb. 2020.

[10]. Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh and J. Liang, ”UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation,” in IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 1856-1867, June 2020.

[11]. C. Fan, J. Yi, J. Tao, Z. Tian, B. Liu and Z. Wen, ”Gated Recurrent Fusion With Joint Training Framework for Robust End-to-End Speech Recognition,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 198-209, 2021.

[12]. L. Chai, J. Du, Q. -F. Liu and C. -H. Lee, ”A Cross-Entropy-Guided Measure (CEGM) for Assessing Speech Recognition Performance and Optimizing DNN-Based Speech Enhancement,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 106-117, 2021.

[13]. Z. Zhang, B. Zhong, S. Zhang, Z. Tang, X. Liu and Z. Zhang, ”Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1024-1033.

[14]. N. Wang, W. Zhou, J. Wang and H. Li, ”Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1571-1580.

[15]. YOLOv5, https://github.com/ultralytics/yolov5.

[16]. M. Tan, R. Pang and Q. V. Le, ”EfficientDet: Scalable and Efficient Object Detection,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 10778-10787.

[17]. K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu and C. Xu, ”GhostNet: More Features From Cheap Operations,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 1577- 1586.

[18]. Kubemetes, https://kubernetes.io/.

[19]. L. -C. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, ”DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-848, 1 April 2018.

[20]. L. Sifre and S. Mallat, ”Rigid-motion scattering for texture classification”, arXiv:1403.1687 [cs], Mar. 2014.

[21]. A. Howard et al., ”Searching for MobileNetV3,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314- 1324.

[22]. S. Liu, L. Qi, H. Qin, J. Shi and J. Jia, ”Path Aggregation Network for Instance Segmentation,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 8759-8768.

[23]. T. -Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan and S. Belongie, ´ ”Feature Pyramid Networks for Object Detection,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.

[24]. Real-World Masked Face Dataset, https://github.com/Xzhangyang/Real-World-Masked-Face-Dataset.

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