Fast Forest Fire Detection and Segmentation Application for UAV-Assisted Mobile Edge Computing System

The increased frequency of forest fires in recent years has raised concerns about the high cost associated with traditional forest fire prevention methods. To address this issue, this paper presents a novel forest fire detection and segmentation application for UAV-assisted mobile edge computing system. Traditional target detection and segmentation systems for forest fire detection are often large and unsuitable for deployment on edge equipment such as UAVs. Deploying such models on edge gateways can also lead to high costs and delays. To overcome these challenges, this paper proposes a lightweight fire target detection and precision segmentation model that can be used on UAVs and other edge equipment. The proposed algorithm achieves more accurate image segmentation, thereby improving the efficiency of fire location. Additionally, an edge computing system is built to link the feedback of the edge model with the edge gateway, administrators, and other intelligent devices promptly. Extensive experiments with large datasets and in real environments demonstrate the efficacy of the proposed algorithm, effectively enhancing the efficiency of forest inspection and forest fire warning capabilities.