How do License Plate Recognition in Areas of Low-light work?
Best insight from top research papers
License Plate Recognition (LPR) in low-light conditions presents challenges due to limited research and dedicated datasets . To address this, methods like night vision enhancement using Recursive Encoder-Decoder Network (RED-Net) have been proposed, significantly improving recognition accuracy under poor lighting . Additionally, image adaptive enhancement algorithms have been developed to classify and enhance low-light images, improving subsequent license plate recognition . A model combining computer visual processing and convolutional neural networks (CNN) has shown robustness in detecting license plates under dark lighting conditions, achieving high accuracy rates . These advancements in low-light LPR involve innovative techniques like lightweight network architectures, image enhancement algorithms, and neural network recognition to overcome the challenges posed by inadequate lighting conditions.
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| Papers (5) | Insight |
|---|---|
4 Citations | An image adaptive enhancement algorithm under low light conditions is proposed, utilizing deep learning for image classification and enhancement, improving license plate recognition in low-light areas. |
22 Aug 2022 | Night Vision Enhancement for License Plate Recognition uses Recursive Encoder-Decoder Network (RED-Net) and non-reference loss functions to detect and recognize license plates in extremely poor lighting conditions, improving accuracy significantly. |
28 Oct 2020 1 Citations | A novel deep learning-based algorithm combines color space detection and CNN to detect number plates under dark lighting conditions, achieving 98.3% accuracy, as per the research. |
22 Aug 2022 | Night vision enhancement using Recursive Encoder-Decoder Network (RED-Net) and non-reference loss functions improves license plate recognition in low-light conditions, achieving a 72.29% accuracy increase compared to standard methods. |
2 Citations | The paper proposes a low-light License Plate Recognition (LPR) method with a dedicated dataset and lightweight architecture similar to VGG, showing improved performance compared to existing methods. |
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