Uganda Traffic Sign Recognition System Using Deep Learning.
Matovic, Mark Phillip
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Increase in the number of vehicles on road necessitates the use of automated systems for driver assistance. These systems form important components of self-driving vehicles also. Traffic Sign Recognition system is such an automated system which provides contextual awareness for the self-driving vehicle. CNN based methods like Faster R-CNN for object detection provide human level accuracy and real time performance and are proven successful in Traffic Sign Recognition systems. Single stage detection systems such as YOLO and SSD offer state-ofthe-art realtime detection speed. In this report we design a traffic sign recognition system by applying deep learning techniques in particular using the Sinlge-Shot Detector algorithm. The network training and evaluation are done using a Ugandan traffic sign detection dataset that we collected ourselves as part of the project. We detect only a subset of the Ugandan traffic signs considering the shortage of data for some traffic signs.