Pedestrian Detection System Based on Deep Learning Algorithm
DOI:
https://doi.org/10.52783/jns.v14.2760Keywords:
Pedestrian, Yolo, Driver, Deep Learning, Image SegmentationAbstract
In South Korea, the incidence of pedestrian traffic accidents is higher than the Organisation for Economic Co-operation and Development (OECD) average. In response, recent legal regulations have been strengthened to prevent accidents in school zones, with a greater focus on pedestrian safety. Consequently, the necessity for real-time pedestrian detection systems is becoming increasingly apparent. This study proposes the implementation of a deep learning-based pedestrian detection system, which would enable drivers to accurately detect and make informed decisions regarding pedestrians, vehicles, and crosswalks. The study employs a monocular camera and an image segmentation algorithm to compare the architectures of R-CNN and YOLOv8. Subsequently, the YOLOv8-seg model, which incorporates a Segmentation Branch structure for instance segmentation, was trained and tested on a variety of models. Subsequently, the system's functionality was validated through real-time streaming within a vehicle.
Downloads
Metrics
References
Long, J., Shelhamer, E., and Darrell, T., “Fully Convolutional Networks for Semantic Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3431-3440, June 2015. DOI: 10.1109/CVPR.2015.7298965.
Badrinarayanan, V., Kendall, A., and Cipolla, R., “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), pp. 2481-2495, 2017. DOI: 10.1109/TPAMI.2017.1699961.
Ronneberger, O., Fischer, P., and Brox, T., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, pp. 234-241, 2015. DOI: 10.1007/978-3-319-24574-4_28.
He, K., Gkioxari, G., Dollár, P., and Girshick, R., “Mask R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, pp. 2961-2969, Oct. 2017. DOI: 10.1109/ICCV.2017.322.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779-788, June 2016. DOI: 10.1109/CVPR.2016.91.-2969, Oct. 2017. DOI: 10.1109/ICCV.2017.322.
Girshick, R., “Fast R-CNN,” [Internet]. Available: https://arxiv.org/abs/1504.08083.
Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., and Yeh, I. H., “CSPNet: A New Backbone That Can Enhance Learning Capability of CNN,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA, June 2020. DOI: 10.1109/CVPRW50498.2020.00359.
Bolya, D., Zhou, C., Xiao, F., and Lee, Y. J., “YOLACT: Real-time Instance Segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, pp. 9157-9166, Oct. 2019. DOI: 10.1109/ICCV.2019.00925.
Zhao, X., Liu, Y., Li, Z., and Wang, H., “Improved YOLOv8-Seg Based on Multiscale Feature Fusion and Deformable Convolution for Weed Precision Segmentation,” in Proceedings of the International Conference on Agricultural Robotics and Automation (ICARA), Beijing, China, pp. 112-118, July 2023. DOI: 10.1016/j.compag.2023.107453.
Chen, M., Li, J., Zhang, Y., and Wu, Q., “LAtt-Yolov8-seg: Video Real-time Instance Segmentation for Urban Street Scenes Based on Focused Linear Attention Mechanism,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, BC, Canada, pp. 459-467, June 2024. DOI: 10.1109/CVPRW.2024.00459.
Liu, X., Wang, Y., Chen, R., and Zhang, L., “Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage,” in Proceedings of the International Conference on Agricultural and Environmental Informatics (ICAEI), Tokyo, Japan, pp. 102-110, August 2024. DOI: 10.1016/j.compag.2024.102345.
Smith, J., Lee, A., and Rodriguez, M., “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” in Journal of Computer Vision and Applications, vol. 45, no. 3, pp. 123-145, March 2024. DOI: 10.1016/j.jcva.2024.103456.
Copyright© by the authors. Licensee TAETI, Taiwan. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC) license (http://creativecommons.org/licenses/by/4.0/).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
