A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition

• URL: http://arxiv.org/abs/2501.07808v1
• Date: Tue, 14 Jan 2025 03:19:10 GMT
• Title: A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition
• Authors: Mingke Xiao, Yue Su, Liang Yu, Guanglong Qu, Yutong Jia, Yukuan Chang, Xu Zhang,
• Abstract summary: We propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment. The proposed model shows excellent recognition performance with an accuracy of up to 97.64%. Our research shows the great potential of BNN in the hardware deployment of computer vision models.
• Score: 5.296139403757585
• License:
• Abstract: The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area that has attracted much attention. With the continuous evolution of deep learning technology, many image classification models are committed to improving recognition accuracy, but this is often accompanied by problems such as large model resource usage, complex structure, and high power consumption, which makes it challenging to deploy on resource-constrained platforms. Herein, we propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment, and conduct image classification research based on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. In addition, we also verify it on the Chinese Traffic Sign (CTS) and Belgian Traffic Sign (BTS) datasets. The proposed model shows excellent recognition performance with an accuracy of up to 97.64%, making it one of the best performing BNN models in the GTSRB dataset. Compared with the full-precision model, the accuracy loss is controlled within 1%, and the parameter storage overhead of the model is only 10% of that of the full-precision model. More importantly, our network model only relies on logical operations and low-bit width fixed-point addition and subtraction operations during the inference phase, which greatly simplifies the design complexity of the processing element (PE). Our research shows the great potential of BNN in the hardware deployment of computer vision models, especially in the field of computer vision tasks related to autonomous driving.

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