用于室内停车场数据集的新型车牌去识别方法

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Seung Ho Nam, Hong Seong Park
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引用次数: 0

摘要

本文探讨了对自动驾驶和机器人研究至关重要的基于图像的数据集中车辆牌照的去识别问题。用于隐私保护的模糊和遮蔽等传统方法会掩盖车牌等关键特征,从而降低数据质量,限制了数据集的实用性。为了克服这一问题,本文介绍了一种新方法,通过结合检测、顶点识别和生成与原始纹理相似的虚拟车牌,在匿名化车牌的同时保留原始数据质量。该方法利用 YOLOv8x 和 Swin Transformer 等先进模型,并通过加权盒融合组合技术加以增强,从而提高了在光照不足和车辆位置多变的复杂室内停车场环境中车牌的检测精度。这种方法提高了在单张图像中检测多个车牌的准确性,并将只有一辆车的图像分离出来进行进一步处理。RetinaFace 模型可识别车牌的精确位置(或 4 个顶点),透视变换技术可使用识别出的顶点将原始车牌替换为虚拟车牌。这些虚拟车牌是随机生成的,但一开始与原始车牌的纹理并不匹配,显然是人为的。为了解决这个问题,一个经过微调的 CycleGAN 模型会调整虚拟车牌的纹理,使其与原始车牌非常相似。最后,将修改后的车牌与原始车辆图像合并,确保数据集既能保持与原始图像的视觉相似性,又能有效地对车牌进行隐私去识别。通过在室内停车场环境中与各种深度学习模型的性能比较,验证了所提出的去识别方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A New License Plate De-Identification Method for Indoor Parking Lot Datasets

A New License Plate De-Identification Method for Indoor Parking Lot Datasets

This paper addresses de-identification problem of vehicle license plates in image-based datasets crucial for autonomous driving and robotics research. Traditional methods such as blurring and masking used for privacy protection reduce data quality by obscuring key features like license plates, limiting the utility of datasets. To overcome this, this paper introduces a new method that retains original data quality while anonymizing license plates by combining detection, vertices identification, and generation of virtual license plates resembling textures of the original. The approach utilizes advanced models such as YOLOv8x and Swin Transformer, enhanced by the Weighted Box Fusion ensemble technique, to improve the detection accuracy of license plates in complex indoor parking lot environments characterized by poor lighting and varied vehicle positions. This method enhances the accuracy of detecting multiple license plates in a single image, isolating images with only one vehicle for further processing. The RetinaFace model identifies the precise positions (or 4 vertices) of license plates and the perspective transformation technique replaces the original plates with virtual ones using the identified vertices. These virtual plates are randomly generated but initially do not match the original plates' texture, making them obviously artificial. To address this, a finely adjusted CycleGAN model adapts the texture of the virtual plates to closely resemble the originals. Finally, the modified plates are merged back with the original vehicle images, ensuring that the dataset retains the visual similarity to the original images while effectively making de-identification of the license plates for privacy. The effectiveness of the proposed de-identification method is validated by comparing its performance with various deep learning models in an indoor parking environment.

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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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