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低能见度下GYOLOv5-SPD算法的车牌检测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
车牌自动检测技术是构建智慧城市、加强交通管理等方面的重要内容.目前车牌检测技术正在逐步完善,但对于光照条件过低、雨天、雪天等能见度极低的气候环境下所拍摄的车牌照片,车牌检测技术还处于一个相对落后的水平.本文在中国城市停车数据集(CCPD)中能见度极低的车牌图像的基础上利用添加了伽玛(Gamma)变换的你只需看一次(YOLO)v5s模型与空间到深度层及无步长卷积层(SPD-Conv)进行融合用于车牌检测,将低能见度条件下的车牌视为小物体进行检测,目的是最大限度地识别其特征以提高精度.实验结果表明,本文用到的方法在车牌定位检测阶段达到了99.68%的召回率,在低能见度条件下对车牌的定位检测相较其他算法确实有一定的优势.
License plate detection in low visibility based on GYOLOv5-SPD algorithm
Automatic license plate detection technology is an important element in building smart cities and strengthening traffic management.At present,license plate detection technology is gradually improving,but it is still at a relatively backward level facing license plate photos taken in extremely low visibility conditions such as low light conditions and rainy or snowy weather.This paper fused the YOLOv5s model with Gamma transform with space-to-depth(SPD)-layer followed by a non-strided convolution(Conv)for license plate detection based on license plate photos taken in extremely low visibility in the Chinese City Parking Dataset(CCPD)and treated license plates in low visibility conditions as small objects for detection,so as to identify their features to maximize accuracy.The experimental results show that the method used in this paper achieves a recall rate of 99.68%in the license plate location detection phase,and the location detection of license plates in low visibility conditions does have some advantages over other algorithms.

license plate detectionspace-to-depth(SPD)layer followed by a non-strided convolution(Conv)Gamma transformGYOLOv5-SPDlow visibility

李泽、李小龙、杨忠祥、谭永滨

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东华理工大学 测绘与空间信息工程学院,江西 南昌 330013

东华理工大学中核三维地理信息工程研究中心,江西 南昌 330013

自然资源部环鄱阳湖矿山环境监测与治理重点实验室,江西 南昌 330013

云南旅游学院 资源工程学院,云南 昆明 650221

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车牌检测 SPD-Conv Gamma变换 GYOLOv5-SPD 低能见度

国家自然科学基金江西省重点研发计划江西省地质局科技研究项目

422610784236106720223BBE510302022JXDZKJKY08

2024

10.19580/j.cnki.1007-3000.2024.05.002

北京测绘
北京市测绘设计研究院,北京测绘学会

北京测绘

影响因子：0.55
ISSN：1007-3000
年,卷(期)：2024.38(5)
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