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Identification of Road Surface Conditions using IoT Sensors and Machine Learning

机译：使用物联网传感器和机器学习识别路面条件

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The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using κ-Nearest Neighbour (κ-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.
机译：本研究的目的是利用物联网（物联网）传感器来收集和分析马来西亚的路面条件，以及可以识别这些条件的机器学习模型的开发。这允许促进低成本的数据采集和知识决策，帮助局部当局进行维修和资源分配。本研究中考虑的条件包括光滑的表面，不均匀的表面，坑洼，速度凸块和隆隆条。考虑到统计特征，如最小，最大，标准偏差，中值，平均，偏移和峰值，两次和频率域形式。使用Ranker，贪婪算法和粒子群优化（PSO）进行特征的选择，然后使用κ-最近邻（κ-nn），随机森林（rf）和带有线性和多项式内核的支持向量机（SVM）进行分类。该模型能够实现99％的准确性，强调模型的有效性来识别这些条件。

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