一种基于改进YOLOv7网络的非机动车车牌检测方法

By improving the YOLOv7 network, constructing the PlateNet model and training it on a dataset, the efficiency and accuracy issues in non-motorized vehicle license plate detection were resolved, achieving efficient non-motorized vehicle license plate localization and detection.

CN117392654BActive Publication Date: 2026-07-17GUILIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GUILIN UNIVERSITY OF TECHNOLOGY
Filing Date
2023-10-08
Publication Date
2026-07-17

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Abstract

本发明专利公开了一种基于改进YOLOv7网络的非机动车车牌检测方法。通过在非机动车车道上录制视频数据的方法采集非机动车车牌样本并制作非机动车车牌检测数据集（NMLP）。以YOLOv7网络为基础，将底层信息引入到特征图上采样过程中生成语义对齐模块，同时为网络深层重新设计一个层次优化模块，形成PlateNet网络。利用K‑means++算法对非机动车车牌重新聚类，将得到的新的目标候选框应用到网络中，并在自制NMLP数据集上进行训练与检测，保存训练最优模型用于真实非机动车车牌检测场景中。本发明专利能够实现精准的非机动车车牌检测与定位，为真实场景下非机动车车牌检测提供了技术支持。
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