基于改进VGG-16网络的列车车次识别

Junlin Zhu, Z. Xing, Yu Duan, Zhenyu Zhang
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引用次数: 0

摘要

随着基于深度学习图像处理技术的列车车次识别系统逐渐得到认可和应用,开发了一种改进的VGG-16网络用于列车车次识别。在经典的VGG-16网络中加入批量标准化(BN),设计了列车车号字符识别方法。结合广州地铁、南京地铁的车次图像,结合实验室对车次识别算法进行了训练和测试。实验结果表明,该方法的列车车号识别综合准确率达到99.54%,满足现场使用要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rail Train Number Recognition Based on Improved VGG-16 Network
As the rail train number recognition system based on deep learning image processing technology was gradually recognized and applied, an improved VGG-16 network was developed for rail train number recognition. The batch standardization (BN) is added to the classical VGG-16 network, and a rail train number character recognition method is designed. Combining with the train number images taken in the Guangzhou Metro, Nanjing Metro, and the laboratory, the train number recognition algorithm is trained and tested. The experimental results show that the comprehensive accuracy rate of the developed method for rail train number recognition reaches 99.54%, which meets the requirements of on-site use.
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