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

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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.

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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.

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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

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  1. A. Devipriya
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  5. Shamimul Qamar
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  6. Abdul Azeem
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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.

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Correspondence to Laxman Singh.

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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

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

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