Lightweight 3D Object Detection Based on Bridging Structure

Xinmeng Wei, Yangming Guo, Jiang Long, Mengxuan Liu, Sheng Lu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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%.

源语言英语
主期刊名Proceedings - 2023 China Automation Congress, CAC 2023
出版商Institute of Electrical and Electronics Engineers Inc.
1205-1210
页数6
ISBN(电子版)9798350303759
DOI
出版状态已出版 - 2023
活动2023 China Automation Congress, CAC 2023 - Chongqing, 中国
期限: 17 11月 202319 11月 2023

出版系列

姓名Proceedings - 2023 China Automation Congress, CAC 2023

会议

会议2023 China Automation Congress, CAC 2023
国家/地区中国
Chongqing
时期17/11/2319/11/23

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