DEEP LEARNING MODEL FOR PERFORMANCE IMPROVEMENT IN FUTURE ENABLED AI-CAMERAS

Authors

  • K. Ramadevi, Karampuri Shiva Sai, Nagavelli Akhil, Gaddala Preethi, Bandi Kushal

Keywords:

Keywords: Image classification, Object identification, Deep learning, Convolutional neural networks, CIFAR-10 dataset.

Abstract

AI enabled cameras have proven to be invaluable tools for various applications including surveillance,autonomous vehicles and augmented reality. These cameras leverage deep learning models classifyobjects accurately and efficiently by using Cifar-10 dataset. Traditional computer vision techniques likerule-based algorithm often struggle with complex scenarios and variations in lighting conditions

References

Taşyürek, Murat. "Odrp: a new approach for spatial street sign detection from exif using deep learning-based object detection, distance estimation, rotation and projection system." The Visual Computer 40, no. 2 (2024): 983-1003.

Yelleni, Sai Harsha, Deepshikha Kumari, and P. K. Srijith. "Monte Carlo DropBlock for modeling uncertainty in object detection." Pattern Recognition 146 (2024): 110003.

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Published

2025-04-12

How to Cite

K. Ramadevi, Karampuri Shiva Sai, Nagavelli Akhil, Gaddala Preethi, Bandi Kushal. (2025). DEEP LEARNING MODEL FOR PERFORMANCE IMPROVEMENT IN FUTURE ENABLED AI-CAMERAS . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 453–461. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2317

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Articles

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