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- Vol. 5 Issue 5
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AI Technology to Enhance the Safety and Security of Heavy Duty Vehicles
Authors
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2 Associate professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 5 (2025)
- Published Online
- September-October 2025
- Pages
- 54-60
- Article Type
- Original Article
- Check for updates
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References
1. Y. Zhang, L. Wang, Z. Liu, and H. Chen, “A Real-Time Object Detection Algorithm for Intelligent Vehicles Using YOLO,” IEEE Access, vol. 7, pp. 67200–67210, 2019.
2. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv: 1804.02767, 2018.
3. R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440–1448.
4. W. Liu et al., “SSD: Single Shot MultiBox Detector,” in European Conference on Computer Vision (ECCV), 2016, pp. 21–37.
5. A. Dosovitskiy et al., “An End-to-End Learning Framework for Self-Driving Cars,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
6. S. Rana, A. Roy, and S. Paul, “Deep Learning Based Smart Surveillance System for Vehicles Using YOLO and OpenCV,” in 2020 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1172–1176.
7. M. A. Hossain, M. A. Mahmud, and K. M. Iftekharuddin, “Driver Behavior Analysis Using Deep Learning for Enhanced Road Safety,” in 2021 IEEE International Conference on Electro Information Technology (EIT), pp. 523–528.
8. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv: 1409.1556, 2014.
9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NIPS), 2012.
10. P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2014
2. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv: 1804.02767, 2018.
3. R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440–1448.
4. W. Liu et al., “SSD: Single Shot MultiBox Detector,” in European Conference on Computer Vision (ECCV), 2016, pp. 21–37.
5. A. Dosovitskiy et al., “An End-to-End Learning Framework for Self-Driving Cars,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
6. S. Rana, A. Roy, and S. Paul, “Deep Learning Based Smart Surveillance System for Vehicles Using YOLO and OpenCV,” in 2020 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1172–1176.
7. M. A. Hossain, M. A. Mahmud, and K. M. Iftekharuddin, “Driver Behavior Analysis Using Deep Learning for Enhanced Road Safety,” in 2021 IEEE International Conference on Electro Information Technology (EIT), pp. 523–528.
8. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv: 1409.1556, 2014.
9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NIPS), 2012.
10. P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2014
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