机载仪器光学检测文本区域自动选择方法

Siyuan Wu, Jieyi Liu, Hongliang Luo, Zhao Nie, Hao Li, Jie Wu
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引用次数: 2

摘要

在机载仪器场景文本区域选择中,指针、网格等类字符元素对文本检测有不利影响,现有的文本检测方法难以处理。本文提出了一种基于全连接神经网络和U-net的改进文本检测模型,该模型具有更少的噪声像素、更少的错误预测区域和更高的空间一致性,具有更好的预测性能。为了进一步解决FCN缺乏空间一致性的问题,在后处理中提出了一种利用种子锚点滤波误报的方法。仿真结果表明,改进的FCN文本检测模型在准确率和查全率方面都优于原全连接神经网络。此外,所提出的后处理方法进一步提高了精度指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automatic Text Region Selection Method on Optical Inspection for Airborne Instrument
For text region selection on airborne instruments scene, the character-like elements such as pointers and grids have a negative effect on text detection, and the existing text detection methods are difficult to handle it. This paper proposes a modified text detection model based on Fully Connected Neural Network and U-net, which achieved better prediction performance of fewer noise pixels, fewer wrongly predicted areas and have relatively higher spatial consistency. To further address the problem of FCN lacking spatial consistency, a method of filtering False Positives by seed anchor was proposed in post processing. The simulation result shows that the improved FCN text detection model performs better than the original Fully Connected Neural Network in both precision and recall. Furthermore, the proposed post processing method further improved precision index.
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