Research on intelligent fire image detection based on FPGA

Bingrui Guo; Bianlian Zhang; Xiaoli Zhang; Senlin Yang

doi:10.1117/12.2662538

12 April 2023 Research on intelligent fire image detection based on FPGA

Bingrui Guo, Bianlian Zhang, Xiaoli Zhang, Senlin Yang

Proceedings Volume 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022); 125651X (2023) https://doi.org/10.1117/12.2662538
Event: Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 2022, Shanghai, China

Abstract

In order to improve the operation efficiency of fire image recognition neural network, the FPGA hardware acceleration of fire image recognition neural network is studied and implemented. Firstly, with the help of fire image database and TensonFlow machine learning platform, a fire image recognition neural network is trained with VGG19 as the neural network model. Then the FPGA hardware design of convolution layer, pooling layer, full connection layer and activation function is carried out for the trained neural network through Vivado. Secondly, the designed VGG19 fire image recognition convolution neural network accelerator is debugged on the ZYNQ7020 development board. Finally, the acceleration performance of fire identification convolutional neural network accelerator system is tested in three aspects: acceleration efficiency, resource utilization and power consumption. The experimental results show that the accelerator can reduce the clock cycle required by each convolution layer of fire image recognition neural network from one million to ten thousand, the resource utilization meets the chip requirements, and the chip power consumption is reduced to 2.067w. While improving the operation efficiency of neural network, it realizes low power consumption.

Citation Download Citation

Bingrui Guo, Bianlian Zhang, Xiaoli Zhang, and Senlin Yang "Research on intelligent fire image detection based on FPGA", Proc. SPIE 12565, Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022), 125651X (12 April 2023); https://doi.org/10.1117/12.2662538

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