HubNet:使用全球和本地特征进行轮文本检测和识别的E2E模型.
1Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究介绍了HubNet,这是一种用于自动检测和识别轮中文的新型模型. 通过整合工业应用的全球和本地特征,HubNet提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 由于文本模糊和方向变化,轮文本识别具有挑战性.
- 准确的产品信息记录对于工业效率至关重要.
研究的目的:
- 开发一个有效的自动轮文本检测和识别模型.
- 为培训和评估此类模型创建一个全面的数据集.
主要方法:
- 一个轮文本数据集的构建,包含来自工业生产线的446张图像.
- 关于HubNet的建议,这是一个端到端的模型,具有新的功能交叉融合模块.
- 利用全球和本地功能来提高文本识别准确度.
主要成果:
- 在定制数据集上,HubNet实现了86.5%的准确率,79.4%的回忆率和0.828的F1得分.
- 功能交叉融合模块提高了精度2%至4%.
结论:
- 开发的HubNet模型和数据集为自动轮文本处理提供了宝贵的参考.
- 提出的方法显著提高了记录轮信息的效率和准确性.
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