Vehicle Embedded Traffic Sign Recognition
ID：147
Submission ID：85 View Protection：ATTENDEE
Updated Time：2024-10-23 10:02:35 Hits：39
Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
No files
Abstract
In response to the challenges encountered in traffic sign detection and recognition, such as small target size, variable shape, difficulty in feature extraction, and susceptibility to complex background, this study proposed a lightweight network model called SCF-YOLOv5. The algorithm integrates the channel attention mechanism before the SPPF layer of the backbone network of the YOLOv5 model, which enhances the model's ability to identify key information. In the neck network, a lightweight general up-sampling operator CARAFE was used instead of traditional up-sampling techniques, improving image resolution through content-aware technology. Furthermore, the CIOU loss function was optimized to Focal loss, effectively addressing class imbalance and sample imbalance issues. Finally, the algorithm was deployed on the Raspberry PI embedded platform. Compared with YOLOv5s, the number of parameters decreased by 40.25%, FPS increased by 11.04%, and mean average precision increased by 3.6%, which enhanced the accuracy and robustness of the algorithm.
Keywords
deep learning, traffic sign recognition, yolov5 improvement, intelligent vehicle I.INTRODU
Speaker
ZhangWenjie
student Hefei Normal UniversitySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
Contact Us
Website:
https://icsmd2024.aconf.org/
Comment submit