@inproceedings{a2ee266c63c14a04a7a5b7aa82b53221,
title = "Lightweight 3D Object Detection Based on Bridging Structure",
abstract = "The study of 3D object detection based on deep learning has increasingly grown in importance in the realm of autonomous driving technology. In this study, we developed a lightweight 3D object detection model based on bridge structure while maintaining the model's accuracy. The model has a much smaller number of parameters. The SGE attention mechanism and the cross-attention mechanism module are first integrated into a backbone network that enhances the lightweight CNN's global semantic information. Second, the loss of target details is prevented thanks to a new bridging structure. Finally, the model's parameter count is decreased using the weight fusion recovery training-based model pruning technique. According to the experimental findings, the model's calculation amount is decreased by 50% while its detection accuracy is raised by 1% to 2%.",
keywords = "3D object detection, CNN, lightweight",
author = "Xinmeng Wei and Yangming Guo and Jiang Long and Mengxuan Liu and Sheng Lu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 China Automation Congress, CAC 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2023",
doi = "10.1109/CAC59555.2023.10452007",
language = "英语",
series = "Proceedings - 2023 China Automation Congress, CAC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1205--1210",
booktitle = "Proceedings - 2023 China Automation Congress, CAC 2023",
}