  Recent work done on traffic sign and traffic light detection focus on
improving detection accuracy in complex scenarios, yet many fail to deliver
real-time performance, specifically with limited computational resources. In
this work, we propose a simple deep learning based end-to-end detection
framework, which effectively tackles challenges inherent to traffic sign and
traffic light detection such as small size, large number of classes and complex
road scenarios. We optimize the detection models using TensorRT and integrate
with Robot Operating System to deploy on an Nvidia Jetson AGX Xavier as our
embedded device. The overall system achieves a high inference speed of 63
frames per second, demonstrating the capability of our system to perform in
real-time. Furthermore, we introduce CeyRo, which is the first ever large-scale
traffic sign and traffic light detection dataset for the Sri Lankan context.
Our dataset consists of 7984 total images with 10176 traffic sign and traffic
light instances covering 70 traffic sign and 5 traffic light classes. The
images have a high resolution of 1920 x 1080 and capture a wide range of
challenging road scenarios with different weather and lighting conditions. Our
work is publicly available at https://github.com/oshadajay/CeyRo.
