The field of Autonomous Driving has witnessed rapid improvement and innovation
in recent years. This has been driven to a large degree by the major advances
in Machine Learning and Object Detection of the previous decade. Deep
Neural Networks enable the vehicle to find solutions to the myriad of complex
and interconnected tasks associated with Autonomous Driving. One such essential
task is the reliable detection and classification of traffic signs, which is also
significant for advanced driver assistance systems.
This thesis will focus on a new approach utilizing the current state-of-the-art
object detection algorithms trained on public datasets. The proposed system
combines YOLOv7 with a custom classifier trained on the German Traffic Signs
dataset. The results are evaluated on the GTSDB dataset and fisheye footage
recorded by the robotic laboratory’s prototype car. The final full system achieves
high results in most metrics, scoring 99.0 on Recall, Precision, and mAP on the
GTSDB test dataset. But it’s limited by the number of traffic sign types the classifier
can recognize.