...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part D. Journal of Automobile Engineering >Vehicle license plate recognition method based on deep convolution network in complex road scene
【24h】

Vehicle license plate recognition method based on deep convolution network in complex road scene

机译：基于复杂道路场景深卷积网络的车辆牌照识别方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and in-depth analysis of the principle of deep convolution network, we designed a deep convolution network for Chinese characters, letters, and numbers in the license plate to automatically learn the essential features of the image to make up for the limitation of the artificial feature recognition of the traditional license plate recognition algorithm. At the same time, according to the convolution kernel, downsampling, and nonlinear operation of the deep convolution network, the nonlinear abstraction ability of the license plate character feature is improved. The experimental results show that the proposed method can quickly and accurately identify the license plate character in complex road scenes. The recognition accuracy is better than the existing license plate recognition algorithm, which improves the accuracy of license plate recognition and achieves an ideal license plate recognition effect.
机译：复杂道路场景中的牌照鲁棒识别算法具有理论和实用的价值。现有的牌照识别算法可以在理想的道路场景下实现更好的识别结果，如中等光强度，良好的射击角度和清晰的车牌目标，但在复杂的道路场景中，如快速，牌照老化的速度模糊，照明较低如雨天，需要改善牌照识别算法的有效性。基于车牌识别算法的现实要求和深度卷积网络原理的深入分析，我们为牌照板中的汉字，字母和数字设计了深度卷积网络，以自动学习图像的基本特征弥补传统牌照识别算法的人工特征识别的限制。同时，根据卷积内核，下采样和非线性操作的深度卷积网络，提高了牌照字符功能的非线性抽象能力。实验结果表明，该方法可以快速准确地识别复杂道路场景中的车牌特征。识别精度优于现有的牌照识别算法，这提高了车牌识别的准确性，实现了理想的车牌识别效果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号