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An evaluation of machine learning methods for speed-bump detection on a GoPro dataset.

Johny Marques¹, Raulcezar Alves¹, Henrique C Oliveira²

¹Universidade Federal de Uberlândia, Faculdade de Computação, Av. João Naves de Ávila, 2121, Santa Mônica, 38400-902 Uberlandia, MG, Brazil.

Anais Da Academia Brasileira De Ciencias

|February 24, 2021

Summary

This study introduces a machine learning method to automatically detect road anomalies like speed bumps using a GoPro camera. The Random Forest algorithm achieved over 96% accuracy, enabling detailed vertical road mapping for autonomous navigation.

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Area of Science:

Computer Science
Robotics
Geospatial Analysis

Background:

High-resolution road maps are crucial for autonomous vehicles, especially when sensors fail in adverse weather.
Current mapping methods struggle with real-time updates and detecting vertical road anomalies.

Purpose of the Study:

To develop an automated methodology for mapping road anomalies, specifically speed bumps.
To assess the effectiveness of Machine Learning (ML) algorithms using off-the-shelf camera data.

Main Methods:

Data acquisition using a GoPro camera across various speed bump types.
Application and comparison of three ML classification techniques: Naive Bayes, Multi-Layer Perceptron, and Random Forest (RF).

Main Results:

The Random Forest algorithm demonstrated high classification accuracy exceeding 96% in identifying speed bumps.
The proposed methodology successfully automated the detection of vertical road anomalies from camera data.

Conclusions:

The developed ML-based approach offers a fast and accurate method for creating detailed maps of vertical road anomalies.
This technique has significant potential for integration into surveying vehicles for real-time road mapping updates.