SEDG-Yolov5: A Lightweight Traffic Sign Detection Model Based on Knowledge Distillation
Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited computational capacity, which cannot meet the direct needs of autonomous vehicles for detection model performance and efficiency. To addr...
Main Authors: | Liang Zhao, Zhengjie Wei, Yanting Li, Junwei Jin, Xuan Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-01-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/12/2/305 |
Similar Items
-
A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5
by: Jie Yang, et al.
Published: (2023-01-01) -
A Lightweight Traffic Sign Detection Algorithm Based on Improved YOLOv7
by: Yuwei LI, et al.
Published: (2024-01-01) -
Research on a lightweight electronic component detection method based on knowledge distillation
by: Zilin Xia, et al.
Published: (2023-11-01) -
A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4
by: Yang Gu, et al.
Published: (2022-03-01) -
Lightweight Underwater Target Detection Algorithm Based on Dynamic Sampling Transformer and Knowledge-Distillation Optimization
by: Liang Chen, et al.
Published: (2023-02-01)