  Traffic sign recognition is a very important computer vision task for a
number of real-world applications such as intelligent transportation
surveillance and analysis. While deep neural networks have been demonstrated in
recent years to provide state-of-the-art performance traffic sign recognition,
a key challenge for enabling the widespread deployment of deep neural networks
for embedded traffic sign recognition is the high computational and memory
requirements of such networks. As a consequence, there are significant benefits
in investigating compact deep neural network architectures for traffic sign
recognition that are better suited for embedded devices. In this paper, we
introduce MicronNet, a highly compact deep convolutional neural network for
real-time embedded traffic sign recognition designed based on macroarchitecture
design principles (e.g., spectral macroarchitecture augmentation, parameter
precision optimization, etc.) as well as numerical microarchitecture
optimization strategies. The resulting overall architecture of MicronNet is
thus designed with as few parameters and computations as possible while
maintaining recognition performance, leading to optimized information density
of the proposed network. The resulting MicronNet possesses a model size of just
~1MB and ~510,000 parameters (~27x fewer parameters than state-of-the-art)
while still achieving a human performance level top-1 accuracy of 98.9% on the
German traffic sign recognition benchmark. Furthermore, MicronNet requires just
~10 million multiply-accumulate operations to perform inference, and has a
time-to-compute of just 32.19 ms on a Cortex-A53 high efficiency processor.
These experimental results show that highly compact, optimized deep neural
network architectures can be designed for real-time traffic sign recognition
that are well-suited for embedded scenarios.
