Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition
Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve t...
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| Format: | Article |
| Language: | English |
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Taiwan Association of Engineering and Technology Innovation
2023-04-01
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| Series: | Proceedings of Engineering and Technology Innovation |
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| Online Access: | https://ojs.imeti.org/index.php/PETI/article/view/10594 |
| _version_ | 1797808528531914752 |
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| author | Suvodip Som Pritam Kumar Gayen Sudip Das |
| author_facet | Suvodip Som Pritam Kumar Gayen Sudip Das |
| author_sort | Suvodip Som |
| collection | DOAJ |
| description |
Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems.
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| first_indexed | 2024-03-13T06:38:52Z |
| format | Article |
| id | doaj.art-34628c40cf864c33b99d0e435256bf16 |
| institution | Directory Open Access Journal |
| issn | 2413-7146 2518-833X |
| language | English |
| last_indexed | 2024-03-13T06:38:52Z |
| publishDate | 2023-04-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | Proceedings of Engineering and Technology Innovation |
| spelling | doaj.art-34628c40cf864c33b99d0e435256bf162023-06-08T18:28:35ZengTaiwan Association of Engineering and Technology InnovationProceedings of Engineering and Technology Innovation2413-71462518-833X2023-04-012410.46604/peti.2023.10594Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate RecognitionSuvodip Som0Pritam Kumar Gayen1Sudip Das2Department of Electrical Engineering, Kalyani Government Engineering College, Nadia, West Bengal, IndiaDepartment of Electrical Engineering, Kalyani Government Engineering College, Nadia, West Bengal, IndiaDepartment of Electrical Engineering, JIS College of Engineering, Nadia, West Bengal, India Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems. https://ojs.imeti.org/index.php/PETI/article/view/10594hybrid preprocessweather-based preprocessnon-local means denoisingdark channel prioradaptive histogram equalization |
| spellingShingle | Suvodip Som Pritam Kumar Gayen Sudip Das Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition Proceedings of Engineering and Technology Innovation hybrid preprocess weather-based preprocess non-local means denoising dark channel prior adaptive histogram equalization |
| title | Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition |
| title_full | Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition |
| title_fullStr | Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition |
| title_full_unstemmed | Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition |
| title_short | Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition |
| title_sort | improved preprocessing strategy under different obscure weather conditions for augmenting automatic license plate recognition |
| topic | hybrid preprocess weather-based preprocess non-local means denoising dark channel prior adaptive histogram equalization |
| url | https://ojs.imeti.org/index.php/PETI/article/view/10594 |
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