针对定向文本检测的高效算法基于旋转脱的界限框
Songma Wei1, Minrui Lu2, Bingsan Chen1
1Fujian Key Laboratory of Intelligent Machining Technology and Equipment, Fujian University of Technology, Fuzhou, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了针对工业智能的改进的定向文本检测算法,在诸如密集和任意定向文本等具有挑战性的场景中提高了准确性. 这种新的方法在基准数据集上实现了卓越的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于复杂的文本特征,工业智能应用经常面临着针对性文本检测的挑战.
- 现有的方法在密集分布,面积比较大或任意对齐的文本中难以达到较低的准确性.
研究的目的:
- 为工业智能开发更有效的定向文本检测算法.
- 为了提高检测具有挑战性的文本实例的准确性和稳定性.
主要方法:
- 该算法基于YOLOv5架构,结合了受DenseNet.Net启发的并行交叉规模特征融合方法.
- 引入了一个旋转分离边界检测模块,以增强旋转边界框匹配并提高回归样品质量.
主要成果:
- 在MSRA-TD500数据集上获得了89.2%的精度和88.1%的F1分数.
- 在ICDAR2015数据集上获得了90.6%的准确性和89.3%的F1分数.
- 与最先进的 (SOTA) 文本检测算法相比,已证明具有竞争力的性能.
结论:
- 拟议的算法有效地解决了工业智能定向文本检测的局限性.
- 新的功能融合和边界检测方法显著提高了检测准确性和稳定性.
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