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Automatic Asphalt pavement crack detection and classification using Neural Networks

机译:使用神经网络的自动沥青路面裂缝检测和分类

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Managing of road maintenance is the most complex task for road administrations. The first presumption for the evaluation analysis and correct road construction rehabilitation is to have accurate and up-todate information about road pavement condition. As the pavement condition survey is a critical process, it needs fast and cost-effective methods to collect necessary data. The paper proposes a system for automatic road pavement survey that uses image processing techniques to extract features from road images. A Neural Networks approach is used for detection of regions of images with defects and, further processing also, classifying defects into separate types. Proposed system could be used in the future to replace human labour for identification and classification of defects.
机译:道路养护管理是道路管理部门最复杂的任务。评估分析和正确进行道路施工修复的第一个假设是要获得有关道路路面状况的准确和最新信息。由于路面状况调查是至关重要的过程,因此需要快速且经济高效的方法来收集必要的数据。本文提出了一种自动道路路面测量系统,该系统使用图像处理技术从道路图像中提取特征。神经网络方法用于检测具有缺陷的图像区域,并且还进行进一步处理,将缺陷分类为单独的类型。建议的系统将来可以用来代替人工来识别和分类缺陷。

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