期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2025-06-18 |
| 作品名稱 | Efficient License Plate Alignment and Recognition Using FPGA‑Based Edge Computing |
| 作品名稱（其他語言） | |
| 著者 | Chao-Hsiang Hsiao; Hoi Lee; Yin-Tien Wang; Min-Jie Hsu |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Electronics 14(12), p. 2475 |
| 摘要 | Efficient and accurate license plate recognition (LPR) in unconstrained environments remains a critical challenge, particularly when confronted with skewed imaging angles and the limited computational capabilities of edge devices. In this study, we propose a high-performance, FPGA-based license plate alignment and recognition (LPAR) system to address these issues. Our LPAR system integrates lightweight deep learning models, including YOLOv4-tiny for license plate detection, a refined convolutional pose machine (CPM) for pose estimation and alignment, and a modified LPRNet for character recognition. By restructuring the pose estimation and alignment architectures to enhance the geometric correction of license plates and adding channel and spatial attention mechanisms to LPRNet for better character recognition, the proposed LPAR system improves recognition accuracy from 88.33% to 95.00%. The complete pipeline achieved a processing speed of 2.00 frames per second (FPS) on a resource-constrained FPGA platform, demonstrating its practical viability for real-time deployment in edge computing scenarios. |
| 關鍵字 | license plate recognition (LPR); license plate alignment; FPGA-based edge computing; spatial and channel attention; model optimization and quantization |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | 國內 |
| 通訊作者 | Yin-Tien Wang |
| 審稿制度 | 是 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128540 ) |