A Mobile Application for Speed Bump Sign Detection Using Neural Networks

Matheus Meier Schreiber, Alberto F. De Souza, Claudine Badue, Thiago Oliveira-Santos

Abstract: A significant number of traffic accidents are attributed to poor traffic signalization, including issues related to speed bump signs, which are a crucial element in the driving context to regulate speed limits. In these circumstances, object detection associated with deep learning methodology is rapidly gaining momentum over the Advanced Driver-Assistance Systems (ADAS) context. It has already showed to be very efficient in helping the driver to achieve a safer experience. Exploring this technology, this article presents a mobile application that detects speed bump signs in real time using the smartphone’s back camera. The application generates sound and visual alerts for the driver to increase the chances of a proper reaction to the speed bump. Since mobile phone usually have limited computational capacities, this article leverages the use of a compact deep learning model trained using an artificially generated dataset. Results show that the final model is able to operate with the limited mobile resources available, while achieving a 90.8 percent Average Precision Score.