Vol. 3 No. 1 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Deep Learning for Autonomous Vehicle Nighttime Vision and Navigation

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  • Dr. Andrei Buldas
Dr. Andrei Buldas
Associate Professor of Computer Science, Technical University of Moldova (UTM)
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Published 14-05-2023

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

How to Cite

[1]
Dr. Andrei Buldas, “Deep Learning for Autonomous Vehicle Nighttime Vision and Navigation”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 1, pp. 348–375, May 2023, Accessed: Dec. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/84

Abstract

This allows the subsequent processing algorithm, such as speed limit detection, traffic signal recognizer, license plate reader, vehicle control, etc., to be more accurate and robust regardless of the time of day. Nighttime vision benchmark for autonomous driving is important to evaluate the performance of the developed algorithms. We evaluate convolutional and recursive deep learning approaches for image enhancement tasks with 502 pairs of darkened-lightened images. We further improve the composite attention mechanism to lighten the multidistorted text/industrial images. By utilizing warp-style attention along the deep layer of both convolutional (comp-CNN) and recursive (comp-RNN) models, better enhancement results (94.8dB and 0.069RMSE, 39.7dB and 0.022RMSE) could be achieved in challenging scenarios.

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