Integrating Deep Learning Techniques for Enhanced Object Detection in Self-Driving Cars

Authors

  • Ch Anusha, Vankayala Anil Santhosh, G Suresh

Keywords:

Deep Learning, Computer Vision, Object detection, Autonomous Vehicles.

Abstract

Deep learning has revolutionized computer vision, allowing autonomous cars and other uses. From pattern recognition to Convolutional Neural Networks, this article covers deep learning technology evolution. Deep learning improves object identification accuracy and real-time performance, which is essential for autonomous vehicle safety. Technical issues include data scarcity, high processing costs, and big datasets. Ethics like AI model bias and privacy are also examined. Deep learning model improvements and AI technology integration are discussed in the article's conclusion. It emphasizes the potential for deep learning to transform transportation and the need for tech firms, manufacturers, and regulators to work together to safely deploy autonomous cars.

Downloads

Download data is not yet available.

References

Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.

Ballard, D. H., & Brown, C. M. (1982). Computer Vision. Prentice Hall.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2722-2730).

Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition.

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in neural information processing systems.

Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition.

Downloads

Published

30.10.2024

How to Cite

Ch Anusha. (2024). Integrating Deep Learning Techniques for Enhanced Object Detection in Self-Driving Cars. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5696 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7512

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.