基于YOLO Tiny与Haar Cascade的BRTA认可车牌实时检测云混合模型
A Cloud-Based Hybrid Model for Real-Time Detection of BRTA-Approved Licence Plates Using YOLO Tiny and Haar Cascade
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- Institute of Appropriate Technology（适宜技术研究院）
- Bangladesh University of Engineering and Technology（孟加拉工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出一种结合YOLO Tiny与Haar Cascade的云混合模型，通过动态重训练流水线适应现实条件，实现对孟加拉国BRTA合规车牌的实时准确检测，在资源受限环境中优于传统方法。
中文摘要 AI 辅助
准确的车牌检测对于智能交通系统、收费、停车管理和执法等应用至关重要。在孟加拉国，由于本地化车牌的复杂性以及光照、遮挡、运动模糊和泥土或污垢等障碍等环境因素，这项任务面临独特的挑战。这些挑战常常使传统方法失效。本文提出了一种新颖的混合方法，将YOLO Tiny深度学习模型与Haar-Cascade分类器相结合，用于增强孟加拉语车牌的检测和定位。我们系统的一个关键创新是集成了动态重训练流水线，使模型能够适应不断变化的现实世界条件。这种重训练机制通过在新数据出现时持续提高模型的准确性，显著提升了低置信度场景下的性能。此外，我们开发了一个公开可访问的BRTA合规车牌数据集，该数据集在多样且具有挑战性的条件下采集，以支持这种方法。实验结果表明，我们的方法不仅在检测准确性和计算效率上优于传统模型，而且在资源受限的环境中（尤其是在孟加拉国）确保持续稳定的性能。
英文摘要
Accurate vehicle license plate detection is essential for applications such as intelligent transportation systems, toll collection, parking management, and law enforcement. In Bangladesh, this task presents distinct challenges due to the complexity of localized license plates and environmental factors like lighting, occlusion, motion blur, and obstructions such as dirt or mud. These challenges often render conventional methods ineffective. This paper introduces a novel hybrid approach, combining the YOLO Tiny deep learning model with the Haar-Cascade classifier, for enhanced detection and localization of Bengali license plates. A key innovation of our system is the integration of a dynamic retraining pipeline, which allows the model to adapt to evolving real-world conditions. This retraining mechanism significantly boosts performance in low-confidence scenarios by continuously improving the model's accuracy as new data is encountered. Additionally, a publicly accessible dataset of BRTA-compliant license plates, captured under diverse and challenging conditions, has been developed to support this approach. Experimental results demonstrate that our approach not only achieves superior detection accuracy and computational efficiency over conventional models but also ensures consistent performance in resource-constrained environments, particularly in Bangladesh.
