Speedy Vision-based Human Detection Using Lightweight Deep Learning Network

Authors

  • Gede Erik Aktama Universitas Parna Raya https://orcid.org/0000-0002-5212-414X
  • Franky Manoppo Department of Informatics, Parna Raya University
  • Rosdiana Simbolon Department of Information System, Parna Raya University
  • Adityo Clinton Laloan Department of Information System, Parna Raya University
  • Andreas Sumendap Department of Computer System, Parna Raya University
  • Muhamad Dwisnanto Putro Department of Electrical Engineering, Faculty of Engineering, Sam Ratulangi University https://orcid.org/0000-0002-1785-1018

DOI:

https://doi.org/10.33387/protk.v11i2.7030

Keywords:

Person detection, efficient YOLO, real-time detector, central processing unit, surveillance system

Abstract

Person detection plays a role as the initial system of video surveillance analysis with various implementations, such as activity analysis, person re-id, behavior analysis, and tracking analysis. The demand for efficient models drives a deep learning architecture with a superficial structure that can operate in real-time. You look only once (YOLO) object detection has been presented as an accurate detector that can operate in real-time. The speed limitation, huge computation cost, and abundant parameters still leave vital issues to improve the efficiency of this architecture. Lightweight human detection is proposed by utilizing the YOLOv5n framework. Modifying layer depth promotes a detection system that can operate fast and without stuttering. As a result, the proposed detector has satisfactory performance and is competitive with existing models. It achieves a mAP of 45.2%, closely competing with other person detectors. Additionally, it can run fast without stumbling at 26 frames per second. The detector's speed offers the advantage of this work that it can be feasibly implemented on a cpu device without a graphics accelerator.

Downloads

Download data is not yet available.

Author Biography

Gede Erik Aktama, Universitas Parna Raya

Head of the Informmatin System Study program at Parna Raya University

References

S. Zhu, R. G. Guendel, A. Yarovoy, and F. Fioranelli, “Continuous Human Activity Recognition with Distributed Radar Sensor Networks and CNN-RNN Architectures,†IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, doi: 10.1109/TGRS.2022.3189746.

Y. Tang, X. Yang, X. Jiang, N. Wang, and X. Gao, “Dually Distribution Pulling Network for Cross-Resolution Person Reidentification,†IEEE Trans Cybern, vol. 52, no. 11, pp. 12016–12027, Nov. 2022, doi: 10.1109/TCYB.2021.3077500.

C. Cui and R. Xu, “Multiple Machine Learning Algorithms for Human Smoking Behavior Detection,†in Proceedings - 2022 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 240–244. doi: 10.1109/MLISE57402.2022.00054.

T. Zhou and Y. Liu, “Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration,†IEEE Access, vol. 9, pp. 161181–161193, 2021, doi: 10.1109/ACCESS.2021.3132077.

Q. Bai, J. Xin, M. Yan, Y. Wang, E. Li, and S. Zhao, “An optimized mask-guided mobile pedestrian detection network with millisecond scale,†in Proceedings - 2020 Chinese Automation Congress, CAC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 4975–4980. doi: 10.1109/CAC51589.2020.9326617.

X. Li, X. Luo, H. Hao, F. Chen, and M. Li, “Pedestrian detection method based on multi-scale fusion inception-SSD model,†2020, pp. 1549–1553. doi: 10.1109/ITAIC49862.2020.9338909.

F. B. Setiawan, C. B. Adipradana, and L. H. Pratomo, “Fruit Ripeness Classification System Using Convolutional Neural Network (CNN) Method,†PROtek : Jurnal Ilmiah Teknik Elektro, vol. 10, no. 1, p. 46, Jan. 2023, doi: 10.33387/protk.v10i1.5549.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,†in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Dec. 2016, pp. 770–778. doi: 10.1109/CVPR.2016.90.

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,†Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks.†[Online]. Available: http://code.google.com/p/cuda-convnet/

X. Zhang, S. Cao, and C. Chen, “Scale-Aware Hierarchical Detection Network for Pedestrian Detection,†IEEE Access, vol. 8, pp. 94429–94439, 2020, doi: 10.1109/ACCESS.2020.2995321.

M. D. Putro, L. Kurnianggoro, and K. H. Jo, “High Performance and Efficient Real-Time Face Detector on Central Processing Unit Based on Convolutional Neural Network,†IEEE Trans Industr Inform, vol. 17, no. 7, pp. 4449–4457, Jul. 2021, doi: 10.1109/TII.2020.3022501.

D. Chen, S. Xia, and Y. Zhou, “Pedestrian detection via contour fragments,†in Chinese Control Conference, CCC, IEEE Computer Society, Aug. 2016, pp. 4054–4060. doi: 10.1109/ChiCC.2016.7553986.

C. B. Murthy and M. Farukh Hashmi, “Real Time Pedestrian Detection Using Robust Enhanced Tiny-YOLOv3,†in 2020 IEEE 17th India Council International Conference, INDICON 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020. doi: 10.1109/INDICON49873.2020.9342082.

J. An, M. D. Putro, A. Priadana, Y. Lee, J. Kim, and K. Jo, “YOLOv5 with Dual Attention Network for Object Detection on Drone,†in 2023 International Workshop on Intelligent Systems (IWIS), IEEE, Aug. 2023, pp. 1–6. doi: 10.1109/IWIS58789.2023.10284592.

C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,†Jul. 2022, [Online]. Available: http://arxiv.org/abs/2207.02696

T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection.â€

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,†Jun. 2015, [Online]. Available: http://arxiv.org/abs/1506.01497

G. Jocher et al., “ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements.†Zenodo, Oct. 2020. doi: 10.5281/zenodo.4154370.

M. and B. S. and H. J. and P. P. and R. D. and D. P. and Z. C. L. Lin Tsung-Yi and Maire, “Microsoft COCO: Common Objects in Context,†in Computer Vision – ECCV 2014, T. and S. B. and T. T. Fleet David and Pajdla, Ed., Cham: Springer International Publishing, 2014, pp. 740–755.

M. Everingham, L. Gool, C. K. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes (VOC) Challenge,†Int. J. Comput. Vision, vol. 88, no. 2, pp. 303–338, Jun. 2010, doi: 10.1007/s11263-009-0275-4.

M. Xu, Z. Wang, X. Liu, L. Ma, and A. Shehzad, “An Efficient Pedestrian Detection for Realtime Surveillance Systems Based on Modified YOLOv3,†IEEE Journal of Radio Frequency Identification, vol. 6, pp. 972–976, 2022, doi: 10.1109/JRFID.2022.3212907.

H. H. Nguyen, T. N. Ta, N. C. Nguyen, V. T. Bui, H. M. Pham, and D. M. Nguyen, “YOLO Based Real-Time Human Detection for Smart Video Surveillance at the Edge,†in ICCE 2020 - 2020 IEEE 8th International Conference on Communications and Electronics, Institute of Electrical and Electronics Engineers Inc., Jan. 2021, pp. 439–444. doi: 10.1109/ICCE48956.2021.9352144.

M. D. Putro, D. L. Nguyen, and K. H. Jo, “A CPU-based Pedestrian Detector using Deep Learning for Intelligent Surveillance Systems,†in Proceedings of the IEEE International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICIT48603.2022.10002735.

M. D. Putro, D. L. Nguyen, A. Priadana, and K. H. Jo, “Fast Person Detector with Efficient Multi-level Contextual Block for Supporting Assistive Robot,†in Proceedings - 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems, ICPS 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICPS51978.2022.9816965.

W. Y. Hsu and W. Y. Lin, “Adaptive Fusion of Multi-Scale YOLO for Pedestrian Detection,†IEEE Access, vol. 9, pp. 110063–110073, 2021, doi: 10.1109/ACCESS.2021.3102600.

Downloads

Additional Files

Published

2024-04-24

How to Cite

Aktama, G. E., Manoppo, F., Simbolon, R., Laloan, A. C., Sumendap, A., & Putro, M. D. (2024). Speedy Vision-based Human Detection Using Lightweight Deep Learning Network. Protek : Jurnal Ilmiah Teknik Elektro, 11(2), 97–104. https://doi.org/10.33387/protk.v11i2.7030

Issue

Vol. 11 No. 2 (2024): Protek : Jurnal Ilmiah Teknik Elektro

Section

Electrical, Power and Energy

License

Authors who publish with this journal agree to the following terms:

  1. The copyright of the accepted for publication articles shall be assigned to Protek : Jurnal Ilmiah Teknik Elektro as the publisher of the journal. The intended copyright includes the rights to publish articles in various forms (including reprints).
  2. Protek : Jurnal Ilmiah Teknik Elektro maintain the publishing rights of the published articles.
  3. Authors are permitted to republish or disseminate published articles by sharing the link/DOI of the article at Protek : Jurnal Ilmiah Teknik Elektro. Authors are allowed to use their articles for any legal purposes deemed necessary without written permission from Protek : Jurnal Ilmiah Teknik Elektro with an acknowledgment of initial publication to this journal.

Protek : Jurnal Ilmiah Teknik Elektro by Department of Electrical Engineering, Faculty of Engineering, Universitas Khairun

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Most read articles by the same author(s)

Similar Articles

<< < 4 5 6 7 8 9 10 11 12 13 > >> 

You may also start an advanced similarity search for this article.