Home Journals Articles Updates About
CN
image
收藏切换
Lightweight Parking-Slot Detection Algorithm Based on Collaborative Attention and Graph Neural Network
收藏切换
PDF
Linhui Li1, 2, Shiwei Yuan1, Jing Lian1, 2, Tangpeng Gu1
Automobile Technology | 2023, (11) : 41 - 48
Less
收藏切换
Automobile Technology | 2023, (11): 41-48
Lightweight Parking-Slot Detection Algorithm Based on Collaborative Attention and Graph Neural Network
Full
Linhui Li1, 2, Shiwei Yuan1, Jing Lian1, 2, Tangpeng Gu1
Affiliations
  • 1 School of Automotive Engineering, Dalian University of Technology, Dalian 116024
  • 2 State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024
Published: 2023-11-24 doi: 10.19620/j.cnki.1000-3703.20221220
Outline
收藏切换

In order to improve the real-time and accuracy of parking slot detection in automatic parking, this paper proposed a lightweight parking-slot detection algorithm based on collaborative attention and graph neural network. Firstly, This algorithm used a lightweight network structure and the improved MobileNetV3 as the feature extraction network, obtained the location information and feature information of the parking-slot marker points through depthwise separable convolution, combined them to obtain the fused features of the marker points, then constructed a graph network structure to enhance the internal relationship of the parking-slot marker points, and combined the cooperative attention mechanism to integrate multiple attention. Finally, the algorithm was tested on the public parking-slot dataset PS2.0. The results indicate that the detection accuracy is better than the current mainstream algorithm, the average reasoning speed of each frame of image can reach 10.1 ms, with good accuracy and real-time performance.

Parking-slot detection  /  Collaborative attention  /  Graph neural network  /  Deep learning
Linhui Li, Shiwei Yuan, Jing Lian, Tangpeng Gu. Lightweight Parking-Slot Detection Algorithm Based on Collaborative Attention and Graph Neural Network[J]. Automobile Technology, 2023 , (11) : 41 -48 . DOI: 10.19620/j.cnki.1000-3703.20221220
Appendix
Less
Year 2023 volume Issue 11
PDF
280
122
Cite this Article
BibTeX
Article Info
doi: 10.19620/j.cnki.1000-3703.20221220
  • Online Date：2025-12-07
  • Published：2023-11-24
Article Data
Affiliations
History
  • Revised：2023-03-01
Funding
Affiliations
    1 School of Automotive Engineering, Dalian University of Technology, Dalian 116024
    2 State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024
References
Share
https://castjournals.cast.org.cn/joweb/qcjs/EN/10.19620/j.cnki.1000-3703.20221220
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House