Evaluation of deep-learning based lane detection under low-light environments

Yubing Wu

doi:10.1117/12.2673401

14 April 2023 Evaluation of deep-learning based lane detection under low-light environments

Yubing Wu

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130N (2023) https://doi.org/10.1117/12.2673401
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Lane detection is an important technology in autonomous driving. Currently, the mainstream lane detection methods are based on deep learning. However, most of deep learning-based lane detection models are trained on images captured on sunny days with enough light. It is concerned about their performance when being used to detect lane lines in low-light environments. In this study, images of lane lines captured in the evening and on rainy days are used to evaluate the performance of a representative LaneNet model. The result shows that the model performed badly when detecting lane lines on rainy days or in the evening. When there is enough lamplight in the evening, the performance of the model is better with part of lane lines being detected, but it still cannot detect correctly as it does on sunny days. Two potential future directions are also proposed in this study.

Citation Download Citation

Yubing Wu "Evaluation of deep-learning based lane detection under low-light environments", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130N (14 April 2023); https://doi.org/10.1117/12.2673401

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KEYWORDS

3D modeling

Rain

Convolution

Deep learning

Binary data

Education and training

Environmental sensing

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