A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection
Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, a...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7841 |
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| author | Utsha Saha Binita Saha Md Ashique Imran |
| author_facet | Utsha Saha Binita Saha Md Ashique Imran |
| author_sort | Utsha Saha |
| collection | DOAJ |
| description | Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces a weather-adaptive Convolutional Neural Network (CNN) framework that leverages the YOLOv10 model that is designed to enhance license plate detection in adverse weather conditions. By incorporating weather-specific data augmentation techniques, our framework improves the robustness of ALPR systems under diverse environmental scenarios. We evaluate the effectiveness of this approach using metrics such as precision, recall, F1, mAP50, and mAP50-95 score across various model configurations and augmentation strategies. The results demonstrate a significant improvement in overall detection performance, particularly in challenging weather conditions. This study provides a promising solution for deploying resilient ALPR systems in regions with similar environmental complexities. |
| format | Article |
| id | doaj-art-22f1a64ece4b42ab979f4382052a4be2 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-22f1a64ece4b42ab979f4382052a4be22024-12-13T16:32:57ZengMDPI AGSensors1424-82202024-12-012423784110.3390/s24237841A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate DetectionUtsha Saha0Binita Saha1Md Ashique Imran2Department of Computer Science, North Dakota State University, Fargo, ND 58105, USADepartment of Computer Science, North Dakota State University, Fargo, ND 58105, USAApplied Computer Science, University of Winnipeg, Winnipeg, MB R3B2E9, CanadaAutomatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces a weather-adaptive Convolutional Neural Network (CNN) framework that leverages the YOLOv10 model that is designed to enhance license plate detection in adverse weather conditions. By incorporating weather-specific data augmentation techniques, our framework improves the robustness of ALPR systems under diverse environmental scenarios. We evaluate the effectiveness of this approach using metrics such as precision, recall, F1, mAP50, and mAP50-95 score across various model configurations and augmentation strategies. The results demonstrate a significant improvement in overall detection performance, particularly in challenging weather conditions. This study provides a promising solution for deploying resilient ALPR systems in regions with similar environmental complexities.https://www.mdpi.com/1424-8220/24/23/7841intelligent transport systems (ITS)automatic license plate recognition (ALPR)YOLOv10convolutional neural networks (CNN)object detectiontraffic monitoring |
| spellingShingle | Utsha Saha Binita Saha Md Ashique Imran A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection Sensors intelligent transport systems (ITS) automatic license plate recognition (ALPR) YOLOv10 convolutional neural networks (CNN) object detection traffic monitoring |
| title | A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection |
| title_full | A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection |
| title_fullStr | A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection |
| title_full_unstemmed | A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection |
| title_short | A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection |
| title_sort | weather adaptive convolutional neural network framework for better license plate detection |
| topic | intelligent transport systems (ITS) automatic license plate recognition (ALPR) YOLOv10 convolutional neural networks (CNN) object detection traffic monitoring |
| url | https://www.mdpi.com/1424-8220/24/23/7841 |
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