Effective and Efficient Detection and Interpretation of Road Direction Signs

Road direction signs depict textual and directional information to convey instructions about how to reach a given destination. Detecting and interpreting road direction signs constitutes a key component in self-driving vehicles, therefore we propose a hybrid pipeline that combines deep learning with traditional handcrafted image processing, aiming for a combination of effectiveness and efficiency. Our solution includes a procedure for the identification of the orientation of arrows, generalizing on a wide variety of pictorial styles for direction signs across the globe. Our pipeline is evaluated in terms of accuracy and inference time of its individual steps, demonstrating excellent performance. Experiments are performed over two variants of the pipeline, assuming the availability of different levels of computational resources. We also test the system's dependence on annotated supervision by performing evaluation with a varying number of training instances.