High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform
We developed a license plate recognition platform based on the Zynq-7000 SoC. A field-programmable gate array (FPGA) was used to build a low-power, high-speed neural network. The system leveraged the ARM processor for initial image processing and used standard license plate characters as a training...
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MDPI AG
2025-04-01
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| Series: | Engineering Proceedings |
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| author | Chin-Hsiung Shen Yu-Hsien Wu Shu-Jung Chen Chuan-Yin Yu |
| author_facet | Chin-Hsiung Shen Yu-Hsien Wu Shu-Jung Chen Chuan-Yin Yu |
| author_sort | Chin-Hsiung Shen |
| collection | DOAJ |
| description | We developed a license plate recognition platform based on the Zynq-7000 SoC. A field-programmable gate array (FPGA) was used to build a low-power, high-speed neural network. The system leveraged the ARM processor for initial image processing and used standard license plate characters as a training dataset. After filtering and processing, the images were resized to 28 × 28 pixels in the grayscale format and then transmitted to the FPGA for high-speed recognition. The digital circuit in the FPGA was implemented using Verilog in a deep learning neural network architecture, with the neurons configured as (57, 12, 57, 36) in a hidden layer. The model was trained for 60 epochs. The neural network was also trained with a dataset consisting of 26 English alphabet characters and 10 digits, augmented using image dilation and erosion. Recognition accuracy was 83.33%. Using Vivado, the system was successfully deployed on the Zynq-7000 SoC, demonstrating its potential in intelligent applications. |
| format | Article |
| id | doaj-art-7702c1b38f694df4b0b4cfb6a07d6af3 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-7702c1b38f694df4b0b4cfb6a07d6af32025-08-20T02:20:57ZengMDPI AGEngineering Proceedings2673-45912025-04-019213310.3390/engproc2025092033High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA PlatformChin-Hsiung Shen0Yu-Hsien Wu1Shu-Jung Chen2Chuan-Yin Yu3Department of Mechatronics Engineering, National Changhua University of Education, Chunghua City 500208, TaiwanDepartment of Mechatronics Engineering, National Changhua University of Education, Chunghua City 500208, TaiwanDepartment of Mechatronics Engineering, National Changhua University of Education, Chunghua City 500208, TaiwanDepartment of Mechatronics Engineering, National Changhua University of Education, Chunghua City 500208, TaiwanWe developed a license plate recognition platform based on the Zynq-7000 SoC. A field-programmable gate array (FPGA) was used to build a low-power, high-speed neural network. The system leveraged the ARM processor for initial image processing and used standard license plate characters as a training dataset. After filtering and processing, the images were resized to 28 × 28 pixels in the grayscale format and then transmitted to the FPGA for high-speed recognition. The digital circuit in the FPGA was implemented using Verilog in a deep learning neural network architecture, with the neurons configured as (57, 12, 57, 36) in a hidden layer. The model was trained for 60 epochs. The neural network was also trained with a dataset consisting of 26 English alphabet characters and 10 digits, augmented using image dilation and erosion. Recognition accuracy was 83.33%. Using Vivado, the system was successfully deployed on the Zynq-7000 SoC, demonstrating its potential in intelligent applications.https://www.mdpi.com/2673-4591/92/1/33neural networkZynq-7000 SoClicense plate recognition |
| spellingShingle | Chin-Hsiung Shen Yu-Hsien Wu Shu-Jung Chen Chuan-Yin Yu High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform Engineering Proceedings neural network Zynq-7000 SoC license plate recognition |
| title | High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform |
| title_full | High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform |
| title_fullStr | High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform |
| title_full_unstemmed | High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform |
| title_short | High-Speed-Recognition Artificial Intelligence Chip Based on ARM+FPGA Platform |
| title_sort | high speed recognition artificial intelligence chip based on arm fpga platform |
| topic | neural network Zynq-7000 SoC license plate recognition |
| url | https://www.mdpi.com/2673-4591/92/1/33 |
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