muNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification
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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 muNet, a highly compact deep convolutional neural network for real-time embedded traffic sign recognition based on macroarchitecture design principles as well as microarchitecture optimization strategies. The resulting overall architecture of muNet is thus designed with as few parameters and computations as possible while maintaining recognition performance. The resulting muNet 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 recognition benchmark. Furthermore, muNet requires just ∼10 million multiply-accumulate operations to perform inference. 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.
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