An Approach to Multi-AAV Ship Detection Based on Mobile Edge Computing Scenarios

Autonomous aerial vehicles (AAVs) are widely used for ship tracking and detection tasks. However, the real-time detection performance is limited by AAV battery capacity and computing power, resulting in a short operational duration. To address this challenge, this paper proposes a AAV ship detection system that focuses on two key aspects: algorithm improvement and computational resource allocation. Specifically, we introduce a lightweight ship detection method tailored for multi-AAV scenarios in a mobile edge computing environment. The proposed method first designs a multi-disentangled knowledge distillation approach based on an information decoupling framework and utilizes a newly designed teacher network to enhance the lightweight detection model. The teacher network disentangles two key types of entanglements: the relationship between the convolutional filters and target categories, and the relationship between the foreground and background regions in the feature maps. Additionally, a proximal policy optimization (PPO) reinforcement learning algorithm is designed to enable real-time decision-making for AAV motion, detection accuracy, and computational offloading. Finally, we validate the superiority of the proposed knowledge distillation method and demonstrate the robustness and effectiveness of the AAV path planning algorithm in various scenarios through a series of experiments. Compared to the improved student models YOLOv8-N and YOLOv10-N, our method improves mAP@0.5 by 1.2% and 1.1% on the SeaShips7000 and FVessel validation sets. Furthermore, compared to the existing methods K-Means and DBSCAN, our approach achieves reward values approximately 2.0 times and 1.4 times higher, respectively.