This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.

本文着重介绍了使用配备Raspberry Pi 3 B +，MP​​U 9250的三脚架小车进行路面实时检测的方法，该方法可方便地收集路面数据并实现实时路面检测。首先，利用Raspberry Pi 3 B +和MPU 9250收集六种路面数据。其次，可以通过采用多种机器学习算法，递归神经网络（RNN）和长短期记忆（LSTM）神经来获得分类器。网络。在机器学习分类器中，梯度提升决策树的最高准确率达97.92％，与KNN的最低准确率75.60％相比提高了29.52％。LSTM神经网络的准确率为95.31％，与RNN相比提高了2.79％，准确率为92.52％。最后，分类器被嵌入Raspberry Pi中以实时检测路面，检测时间约为一秒钟。该路面检测系统可用于轮式自动驾驶汽车，并引导自动驾驶汽车平稳移动。