Traffic sign recognition system using deep learning.

Abstract

Traffic signs are an essential part of any transport system, and failure of detection by the driver may significantly increase the accident risk. Traffic sign detection also plays an important role in autonomous driving. In this report we design a traffic sign recognition system by applying deep learning techniques, a subfield of AI and in particular using the SSD algorithm. Compared with other detection systems, this system offers real-time detection and thus can be incorporated in a larger driver assistance system. The objective is locating and classifying traffic signs in natural street scenes, drawing a bounding box around it, identifying the type of traffic sign and relaying this information to the driver dashboard. The machine learning model developed is trained on Ugandan traffic signs collected locally and is thus applicable on Ugandan roads. The test results show that the model accurately locates the traffic signs confirming its robustness and suitability in real-world applications. The system detects only a subset of the Ugandan traffic signs considering the shortage of data for some traffic signs. This will however lay a foundation for subsequent research in traffic sign recognition systems. Traditional Evaluation parameters: mAP(mean Average precision-Precision, Recall and IoU) and FPS(Frames per second) are run-down to analyze the accuracy and speed of the two SSD algorithms trained in this report i.e. one built with a ResNet backbone and one with a MobileNet backbone.