Enterprise AI Analysis
Advanced deep learning techniques for automated license plate recognition
This research significantly enhances automated license plate recognition (ALPR) for Thai-Roman mixed-script license plates, a complex and underexplored problem. By combining YOLOv10 with a custom-tuned Tesseract OCR engine, the system achieves 99.16% detection accuracy and 0.992 F1-score with an inference time of 1.0 ms/image, even under severe lighting and weather conditions. A large-scale dataset of 50,000 images and 10,000 video clips was compiled for robust testing. The system demonstrates real-time capability on Jetson Nano, making it suitable for intelligent transportation systems. Comparative benchmarking against YOLOv5, YOLOv8, YOLOv9, Faster R-CNN, and SSD confirms YOLOv10's superior performance, positioning the proposed system as a novel and practical contribution to regionally adaptive ALPR research.
Revolutionizing Thai ALPR: Key Performance Metrics
Our system, combining YOLOv10 and fine-tuned Tesseract OCR, sets a new standard for accuracy and efficiency in complex ALPR environments.
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Detection Accuracy
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F1-Score
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Inference Time
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Real-time FPS
Deep Analysis & Enterprise Applications
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Methodological Innovation
Dataset Contribution
Comparative Benchmarking
Real-world Deployment
Integration of YOLOv10 with a specially trained Tesseract OCR engine, including robust preprocessing (denoising, contrast normalization, and segmentation), enabling robust identification of Thai-Roman mixed scripts on license plates – a challenge previously limitedly addressed.
Creation of a large-scale dataset with 50,000 photos and 10,000 videos (10-15 seconds each) of Thai automobiles under diverse scenarios (day, night, rain, fog), providing a highly realistic benchmark for non-standardized ALPR contexts.
Extensive experiments comparing YOLOv10 against YOLOv5, YOLOv8, YOLOv9, Faster R-CNN, and SSD, demonstrating YOLOv10's superior performance across all metrics (99.16% accuracy, 0.992 F1-score, 1.0 ms/image inference time) even under adverse conditions.
Successful testing on NVIDIA Jetson Nano, confirming real-time operation at up to 30 FPS, proving its suitability for intelligent transport and smart mobility applications beyond laboratory settings.
99.16%
Overall Detection Accuracy with YOLOv10
Proposed ALPR System Workflow
Input Layer (CCTV, Dashcams)
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Preprocessing (Noise Reduction, Segmentation)
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Detection (YOLOv10)
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Recognition (Fine-tuned Tesseract OCR)
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Data Processing (Tracking, DB Storage)
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API Integration
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Evaluation & Deployment
| Model | Accuracy (%) | F1-Score | Inference Time (ms/image) |
|---|---|---|---|
| YOLOv10 | 99.16 | 0.992 | 1.0 |
| YOLOv9 | 98.96 | 0.988 | 1.4 |
| YOLOv8 | 97.79 | 0.977 | 1.4 |
| YOLOv5 | 96.97 | 0.975 | 1.4 |
| Faster R-CNN | 92.00 | 0.922 | 4.5 |
| SSD | 90.00 | 0.907 | 2.5 |
Edge Deployment with NVIDIA Jetson Nano
The system's successful deployment on NVIDIA Jetson Nano achieved up to 30 FPS real-time processing, demonstrating its practical viability for resource-constrained environments. This makes it ideal for intelligent traffic monitoring and smart city applications, overcoming traditional ALPR limitations in hardware efficiency.
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Your ALPR Implementation Roadmap
Deploying advanced ALPR for Thai license plates involves strategic steps to ensure seamless integration and optimal performance within your existing infrastructure.
Phase 1: Discovery & Customization
Initial consultation to understand specific operational needs, existing infrastructure, and regional plate variations. Data collection augmentation and Tesseract OCR fine-tuning for unique Thai scripts and fonts.
Phase 2: Model Training & Integration
Training YOLOv10 on your enhanced dataset, integrating the custom OCR module. Developing APIs for seamless data flow with existing traffic management or security systems.
Phase 3: Pilot Deployment & Optimization
Small-scale pilot deployment on edge devices (e.g., Jetson Nano) in a controlled environment. Performance monitoring and iterative adjustments for real-time accuracy and robustness.
Phase 4: Full-Scale Rollout & Support
Deployment across all target locations, including comprehensive staff training and ongoing technical support, ensuring long-term operational excellence and scalability.
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