基于深度学习的铁路货车车轮组上倾斜字符的检测和识别
Fengxia Xu1, Zhenyang Xu1, Zhongda Lu1
1School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161000, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
本研究引入了一种深度学习方法,用于检测和识别铁路车轮上的倾斜字符,确保更安全的火车运行. 该系统能够准确地识别关键的生产信息,尽管存在字符倾斜和定位问题.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机械工程 机械工程
背景情况:
- 铁路车轮质量对于安全的火车运行和物流至关重要.
- 目前的检测方法在车轮上与倾斜和可变位置的字符作斗争.
- 准确识别轮生产信息对于调度和安全至关重要.
研究的目的:
- 开发一种基于深度学习的方法,用于准确检测和识别铁路车轮组上倾斜的特征信息.
- 解决基于图像的检测中字符倾斜和不固定位置所带来的挑战.
- 提高生产信息提取的可靠性,以提高铁路安全.
主要方法:
- 一个倾斜的字符检测网络使用更快的RCNN来识别字符候选区域.
- 一个倾斜的字符校正网络,旨在分类和纠正字符方向.
- 一个使用卷积循环神经网络 (CRNN) 进行最终字符识别的字符识别网络.
主要成果:
- 拟议的方法在图像中有效地检测和识别铁路车轮上的倾斜字符.
- 在识别关键生产数据方面表现出高准确性和效率.
- 成功克服了传统方法在角色定位和位置方面的局限性.
结论:
- 深度学习方法为从铁路车轮图像中提取重要信息提供了强大的解决方案.
- 这项技术对提高铁路运输安全和运营效率做出了重大贡献.
- 该方法为铁路行业的自动化检查和数据采集提供了可靠的工具.
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