Application of automatic image enhancing technique to road defect detection systems

ZHANG Da-qi; QU Shi-ru; HE Li

doi:10.3788/OPE.20101808.1869

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Application of automatic image enhancing technique to road defect detection systems

Article | 更新时间：2020-08-12

- Application of automatic image enhancing technique to road defect detection systems
- Optics and Precision Engineering Vol. 18, Issue 8, Pages: 1869-1876(2010)
- 作者机构：
  
  西北工业大学自动化学院,陕西西安,710072
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20101808.1869
  CLC： TP391.4;U416.03
- Received：09 April 2009，
  
  Revised：24 February 2010，
  
  Published Online：20 August 2010，
  
  Published：20 August 2010
- 稿件说明：
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张大奇, 曲仕茹, 何力. 路面病害检测系统中的图像增强技术[J]. 光学精密工程, 2010,18(8): 1869-1876

ZHANG Da-qi, QU Shi-ru, HE Li. Application of automatic image enhancing technique to road defect detection systems[J]. 光学精密工程, 2010,18(8): 1869-1876
张大奇, 曲仕茹, 何力. 路面病害检测系统中的图像增强技术[J]. 光学精密工程, 2010,18(8): 1869-1876 DOI： 10.3788/OPE.20101808.1869.

ZHANG Da-qi, QU Shi-ru, HE Li. Application of automatic image enhancing technique to road defect detection systems[J]. 光学精密工程, 2010,18(8): 1869-1876 DOI： 10.3788/OPE.20101808.1869.

摘要

为了提高路面病害检测系统的精度和自适应能力

研究了自动路面裂缝图像增强技术。针对图像每个像素建立由邻域一致性模糊熵测度、邻域模糊方差以及全局模糊隶属度组成的模糊特征模式对所有像素进行分类

实现对像素灰度渡越点位置的准确估计;在模糊隶属度函数设计上

利用修改控制顶点和权因子可局部地修改曲线形状的功能

通过非均匀有理B样条函数设计出一种双"S"形模糊隶属度函数作为灰度变换函数。提出的路面裂缝图像增强技术中的灰度变换函数不仅能和渡越点的位置很好地结合

而且其形状调节因子具备良好的灰度集中能力

仅需很少次数迭代增强就能达到突出路面裂纹的效果。实验结果显示

利用提出的图像增强技术可使路面裂缝病害像素正确检测率达到95%

该技术很大程度地提高了路面裂缝自动检测系统的可靠性和检测精度。

Abstract

In order to improve precision and adaptive capability of road defect detection system

an image enhancement technique for automatic road defect is investigated. To estimate the crossover points of gray transform

a new 3-D pattern formed with two local features of the neighborhood of each pixel and one global feature based fuzzy measurement is constructed for pixel classification. In design of a fuzzy membership function with image enhancement ability

the Non-uniform Rational B-Splines(NURBS) is used to produce a double S shape fuzzy membership function as the gray transform function. The gray transform function can easily be united with the crossover points perfectly and its designable shape can offer capability of gray level centralization. Therefore

the method can obtain the road crack only by a few operations of iteration and enhancement. Experimental results testify that this adaptive technique can provide satisfactory enhancement effects and the detection accuracy of road crack region pixels reaches 95%. The proposed technique shows better reliability and precision for road defect detection systems.

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Keywords

references

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