Year: 2026
Type: article
Abstract: Multimodal 3D object detection is crucial for autonomous systems but suffers from high delay due to significant computational demands. To address this, we propose an edge computing-assisted framework that balances load between terminal devices and edge servers. We introduce dynamic threshold tuning and resolution-adaptive offloading algorithms to optimize performance. Experimental results demonstrate that our approach significantly reduces delay by minimizing offloading frequency while maintaining high accuracy, achieving a superior delay-accuracy trade-off. Furthermore, the framework exhibits robust adaptability across various models and bandwidth conditions, ensuring effectiveness in dynamic environments.
Source: PLoS ONE
Authors: Kangli Zhao, Zhongrui Gou, Huaqing Liu
Institutions: Aba Teachers University, Southwest Jiaotong University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W7154609580