A cloud-edge-device collaborative architecture for multimodal LLM-based intelligent apron visual surveillance system

Abstract

Intelligent apron visual surveillance requires both high inference accuracy and strict real-time performance. However, directly deploying multimodal large language models (MLLMs) in airport apron scenarios introduces significant challenges in latency, energy consumption, and computational resource allocation. To address these issues, this paper proposes a cloud-edge-device collaborative architecture with hierarchical MLLM deployment for intelligent apron visual surveillance. In the proposed architecture, smart cameras preprocess video streams and generate inference tasks, while lightweight MLLMs are deployed at edge servers and a high-performance MLLM is deployed in the cloud. Furthermore, to fully leverage the performance of this system, a multi-agent proximal policy optimization (MAPPO)-based task offloading algorithm is designed to jointly optimize latency, energy consumption, and inference accuracy under dynamic network and workload conditions. Experimental results demonstrate that the proposed method achieves superior overall performance compared with other baselines, effectively balancing real-time responsiveness and inference accuracy for intelligent apron visual surveillance systems.