Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Machine learning-driven pedestrian detection and classification for electric vehicles: integrating Bayesian component network analysis and reinforcement region-based convolutional neural networks

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Autonomous electric vehicle safety is crucially dependent on the accurate recognition of pedestrians in diverse situations. Current pedestrian detection techniques, however, face significant limitations due to reduced visibility and poor-quality images under low-lighting scenarios. With the aim of overcoming these challenges, this article proposes a novel, sustainable method for pedestrian detection and classification in electric vehicles using machine learning techniques. The approach processes video frame-based images as input, removing noise and smoothing the images for improved detection. A Bayesian component network analysis is employed to refine the features of the filtering-based boundary box detection, further enhancing the detection process. The selected features are then classified using a fully connected kernel operation based on the region with reward Q-Reinforcement architecture, resulting in a secure and efficient pedestrian detection system. The proposed method was evaluated on multiple image datasets using average precision, an area under the curve (AUC), log-average miss rate (MR), and root-mean-square error (RMSE) as performance measures. The experimental results demonstrated an average precision of 92%, MR of 48%, AUC of 56%, and RMSE of 61%. These findings indicate that the proposed technique effectively enhances pedestrian detection and classification for autonomous electric vehicles, contributing to increased safety and reliability in real-world applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availability of data and material

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Code availability

Not applicable.

References

  1. Rahmaniar, W., Hernawan, A.: Real-time human detection using deep learning on embedded platforms: a review. J. Robot. Control (JRC) 2(6), 462–468 (2021)

    Google Scholar 

  2. Tsai, P.F., Liao, C.H., Yuan, S.M.: Using deep learning with thermal imaging for human detection in heavy smoke scenarios. Sensors 22(14), 5351 (2022)

    Article  Google Scholar 

  3. Dewangan, D.K., Sahu, S.P.: Towards the design of vision-based intelligent vehicle system: methodologies and challenges. Evolut. Intell. 16, 1–42 (2022)

    Google Scholar 

  4. Boudjit, K., Ramzan, N.: Human detection based on deep learning YOLO-v2 for real-time UAV applications. J. Exp. Theor. Artif. Intell. 34(3), 527–544 (2022)

    Article  Google Scholar 

  5. Iftikhar, S., Zhang, Z., Asim, M., Muthanna, A., Koucheryavy, A., Abd El-Latif, A.A.: Deep learning-based pedestrian detection in autonomous vehicles: substantial issues and challenges. Electronics 11(21), 3551 (2022)

    Article  Google Scholar 

  6. Ding, L., Wang, Y., Laganière, R., Huang, D., Luo, X., Zhang, H.: A robust and fast multispectral pedestrian detection deep network. Knowl. Based Syst. 227, 106990 (2021)

    Article  Google Scholar 

  7. Ahmed, I., Anisetti, M., Jeon, G.: An IoT-based human detection system for complex industrial environment with deep learning architectures and transfer learning. Int. J. Intell. Syst. 37, 10249–10267 (2021)

    Article  Google Scholar 

  8. Park, S., Kim, J.U., Kim, Y.G., Moon, S.K., Ro, Y.M.: Robust multispectral pedestrian detection via uncertainty-aware cross-modal learning. In: International Conference on Multimedia Modeling, pp. 391–402. Springer, Cham (2021)

  9. Tian, D., Han, Y., Wang, B., Guan, T., Wei, W.: A review of intelligent driving pedestrian detection based on deep learning. Comput. Intell. Neurosci. 2021, 1–16 (2021)

    Google Scholar 

  10. Pattanayak, S., Ningthoujam, C., Pradhan, N.: A survey on pedestrian detection system using computer vision and deep learning. In: Advanced Computational Paradigms and Hybrid Intelligent Computing, pp. 419–429. Springer, Singapore (2022)

  11. Kamboj, A., Rani, R., Nigam, A.: A comprehensive survey and deep learning-based approach for human recognition using ear biometric. Vis. Comput. 38(7), 2383–2416 (2022)

    Article  Google Scholar 

  12. Chen, X., Jia, Y., Tong, X., Li, Z.: Research on pedestrian detection and deepsort tracking in front of intelligent vehicle based on deep learning. Sustainability 14(15), 9281 (2022)

    Article  Google Scholar 

  13. Ahmed, I., Jeon, G., Chehri, A., Hassan, M.M.: Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies. Sustain. Cities Soc. 70, 102908 (2021)

    Article  Google Scholar 

  14. Rizk, M., Slim, F., Baghdadi, A., Diguet, J.P.: Towards real-time human detection in maritime environment using embedded deep learning. In: International Conference on System-Integrated Intelligence, pp. 583–593. Springer, Cham (2023)

  15. Masita, K.L., Hasan, A.N., Shongwe, T.: Refining the efficiency of R-CNN in pedestrian detection. In: Proceedings of Sixth International Congress on Information and Communication Technology, pp. 1–14. Springer, Singapore (2022)

  16. Hajari, K., Gawande, U., Golhar, Y.: Deep learning approach for pedestrian detection, tracking, and suspicious activity recognition in academic environment. In: Intelligent Computing and Applications, pp. 29–38. Springer, Singapore (2023)

  17. Jabłoński, P., Iwaniec, J., Zabierowski, W.: Comparison of pedestrian detectors for LiDAR sensor trained on custom synthetic, real and mixed datasets. Sensors 22(18), 7014 (2022)

    Article  Google Scholar 

  18. Boukerche, A., Sha, M.: Design guidelines on deep learning-based pedestrian detection methods for supporting autonomous vehicles. ACM Comput. Surv. (CSUR) 54(6), 1–36 (2021)

    Article  Google Scholar 

  19. Shah, V., Agarwal, A., Verlekar, T.T., Singh R.: Adapting deep neural networks for pedestrian-detection to low-light conditions without re-training. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2535–2541 (2021)

  20. Su, C.L., Lai, W.C., Te Li, C.: Pedestrian detection system with edge computing integration on embedded vehicle. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 450–453. IEEE (2021)

  21. Sahu, S., Sahu, S.P., Dewangan, D.K.: Pedestrian detection using MobileNetV2 based mask R-CNN. In: IoT Based Control Networks and Intelligent Systems: Proceedings of 3rd ICICNIS 2022, pp. 299–318. Springer, Singapore (2022)

  22. Banjarey, K., Sahu, S.P., Dewangan, D.K.: Human activity recognition using 1D convolutional neural network. In: Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021, pp. 691–702. Springer, Singapore (2022)

  23. Dewangan, D.K., Sahu, S.P.: Lane detection in intelligent vehicle system using optimal 2-tier deep convolutional neural network. Multimed Tools Appl 82, 7293–7317 (2023). https://doi.org/10.1007/s11042-022-13425-7

    Article  Google Scholar 

  24. Dewangan, D.K., Sahu, S.P.: Optimized convolutional neural network for road detection with structured contour and spatial information for intelligent vehicle system. Int. J. Pattern Recogn. Artif. Intell. 36(06), 2252002 (2022)

    Article  Google Scholar 

  25. Dewangan, D.K., Sahu, S.P.: Predictive control strategy for driving of intelligent vehicle system against the parking slots. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 10–13 (2021). https://doi.org/10.1109/ICICCS51141.2021.9432362.

  26. Dewangan, D.K., Sahu, S.P.: Real time object tracking for intelligent vehicle. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India, pp. 134–138 (2020). https://doi.org/10.1109/ICPC2T48082.2020.9071478

  27. Dewangan, D.K., Sahu, S.P.: Lane detection for intelligent vehicle system using image processing techniques. In: Data Science: Theory, Algorithms, and Applications, pp. 329–348 (2021)

  28. Dewangan, D.K., Sahu, S.P.: RCNet: road classification convolutional neural networks for intelligent vehicle system. Intel. Serv. Robot. 14(2), 199–214 (2021)

    Article  Google Scholar 

Download references

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia for funding this work through Large Groups RGP.2/170/1444.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India

    A. Devipriya

  2. Department of Data Science and Business Systems, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu, India

    D. Prabakar

  3. Department of Electronics and Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

    Laxman Singh

  4. Department of Computational Intelligence, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu, India

    A. Sheryl Oliver

  5. Computer Science and Engineering Department, College of Sciences and Arts, King Khalid University, Dhahran Al Janoub Campus, 64261, Abha, Kingdom of Saudi Arabia

    Shamimul Qamar

  6. Electrical Engineering, Jamia Millia Isalmia and Delhi Technological University, New Delhi, India

    Abdul Azeem

Authors
  1. A. Devipriya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Prabakar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laxman Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Sheryl Oliver
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shamimul Qamar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abdul Azeem
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors agreed on the content of the study. AD, DP, LS, ASO, SQ and AA collected all the data for analysis. LS agreed on the methodology. AD, DP, LS, ASO, SQ and AA completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

Corresponding author

Correspondence to Laxman Singh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants.

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devipriya, A., Prabakar, D., Singh, L. et al. Machine learning-driven pedestrian detection and classification for electric vehicles: integrating Bayesian component network analysis and reinforcement region-based convolutional neural networks. SIViP 17, 4475–4483 (2023). https://doi.org/10.1007/s11760-023-02681-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02681-1

Keywords

Search

Navigation