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Forest fires pose a serious threat to forest ecosystems, biodiversity, carbon sequestration, and sustainable socioeconomic development. Timely and accurate detection is essential for effective early warning and emergency response. However, existing deep learning-based methods often face challenges in balancing detection accuracy and computational efficiency under UAV-assisted forestry monitoring scenarios. This study proposes YOLO-MR, a compact and efficient forest fire detection framework based on YOLOv8n for UAV-assisted intelligent forestry monitoring. The proposed method incorporates a multi-scale deep dilated spatial pyramid fast pooling module, GhostConv, an improved ReC2f module, and an additional P2 detection layer, as well as PIoUv2 loss, enabling more effective multi-scale feature representation and improved target localization. Evaluation using the public M4SFWD dataset together with the self-constructed UFFD dataset verifies the effectiveness and generalization capability of YOLO-MR. Compared against YOLOv8n, the proposed YOLO-MR delivers increases of 1.3%, 2.1%, 1.3%, and 2.1% for Precision, Recall, mAP50, and mAP50-90, respectively, on the M4SFWD dataset, with corresponding gains of 0.9%, 1.8%, 1.8%, and 2.5% on the UFFD dataset. Meanwhile, the proposed model requires only 2.85 M parameters, which is 5.3% fewer than the baseline, demonstrating improved parameter efficiency. The experimental results demonstrate that YOLO-MR provides an effective trade-off between detection performance and computational efficiency, offering a promising solution for Unmanned Aerial Vehicle (UAV)-assisted forest fire monitoring and early warning. By enabling timely wildfire detection with a compact and efficient architecture, the proposed framework supports sustainable intelligent forestry monitoring, contributes to ecosystem protection and disaster prevention, and promotes sustainable forest management.
Zhong et al. (Fri,) studied this question.