Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

Abstract

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.