Research on Hardware Acceleration of Traffic Sign Recognition Based on Spiking Neural Network and FPGA Platform
Huarun Chen, Yijun Liu, Wujian Ye, Jialiang Ye, Yuehai Chen, Shaozhen Chen, Chao Han · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2024
Most of the existing methods for traffic sign recognition exploited deep learning technology such as convolutional neural networks (CNNs) to achieve a breakthrough in detection accuracy; however, due to the large number of CNN’s parameters, there are problems in practical applications such as high power consumption, large calculation, and slow speed. Compared with CNN, a spiking neural network (SNN) can effectively simulate the information processing mechanism of biological brain, with stronger parallel processing capability, better sparsity, and real-time performance. Thus, we design and realize a novel traffic sign recognition system [called SNN on FPGA-traffic sign recognition system (SFPGA-TSRS)] based on spiking CNN (SCNN) and FPGA platform. Specifically, to improve the recognition accuracy, a traffic sign recognition model spatial attention SCNN (SA-SCNN) is proposed by combining LIF/IF neurons based SCNN with SA mechanism; and to accelerate the model inference, a neuron module is implemented with high performance, and an input coding module is designed as the input layer of the recognition model. The experiments show that compared with existing systems, the proposed SFPGA-TSRS can efficiently support the deployment of SCNN models, with a higher recognition accuracy of 99.22%, a faster frame rate of 66.38 frames per second (FPS), and lower power consumption of 1.423 W on the GTSRB dataset.