  Autonomous driving is becoming a future practical lifestyle greatly driven by
deep learning. Specifically, an effective traffic sign detection by deep
learning plays a critical role for it. However, different countries have
different sets of traffic signs, making localized traffic sign recognition
model training a tedious and daunting task. To address the issues of taking
amount of time to compute complicate algorithm and low ratio of detecting
blurred and sub-pixel images of localized traffic signs, we propose Multi-Scale
Deconvolution Networks (MDN), which flexibly combines multi-scale convolutional
neural network with deconvolution sub-network, leading to efficient and
reliable localized traffic sign recognition model training. It is demonstrated
that the proposed MDN is effective compared with classical algorithms on the
benchmarks of the localized traffic sign, such as Chinese Traffic Sign Dataset
(CTSD), and the German Traffic Sign Benchmarks (GTSRB).
