使用机器学习方法,以标准纹理数据库为基准,对路面类型和使用年限进行自动分类:试点研究

Jiale Lu, Baofeng Pan, Quan Liu, Pengfei Liu, M. Oeser
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引用次数: 0

摘要

路面智能管理系统引起了研究人员的极大兴趣。然而,由于路面类型、质地使用年限等路面使用条件各不相同,因此无法找到一种适用于所有情况的通用算法。因此,路面类型和使用年限的自动分类是解决上述瓶颈的必要前提。基于表面纹理数据,本文介绍了利用卷积神经网络(CNN)识别路面表面纹理的自动分类方法的试验研究。为了进行比较,还研究了支持向量机 (SVM) 的效率。自动分类总共涉及三种情况:(i) 路面类型;(ii) 纹理使用年限;(iii) (i) 和 (ii) 的组合。结果表明,CNN 的表现优于 SVM,CNN 模型对上述三种情况的分类准确率分别为 93.0%、81.1% 和 83.8%。总之,CNN 在表达路面纹理特征方面表现出了很高的能力,在路面表面类型的识别方面取得了令人满意的结果,但在纹理使用年限方面则逊色不少。本文提出的结果有望成为以标准纹理数据库为基准自动识别纹理使用状况的基础性探索,从而促进路面管理系统的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Classification of Pavement Type and Service Age Benchmarked with Standard Texture Databases Using the Machine Learning Method: A Pilot Study
Pavement intelligent management systems have attracted considerable interest from researchers. However, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ii) texture service ages, and (iii) a combination of (i) and (ii), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively. In conclusion, the CNN demonstrates a high capability in expressing the pavement texture features and achieves satisfactory identification results for pavement surface types, but is inferior for texture service age. It is promising that the presented results could serve as a foundational exploration in the automatic identification of texture service conditions benchmarked with standard texture databases to facilitate pavement management systems.
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