MSF-YOLO: A Multi-Scale Feature Enhancement Network for Tiny Fire Spot Detection in UAV Forest Images

Abstract

Tiny fire spot detection in UAV images under complex forest backgrounds remains challenging due to tiny target size, sparse distribution, weak feature responses, and background interference. This paper proposes a Multi-Scale Feature Enhancement Network (MSF-YOLO) for tiny fire spot detection. Specifically, a lightweight C2f-ARG module is designed by integrating Ghost feature generation and channel recalibration mechanisms to enhance weak fire spot representation while reducing redundant features. A C2f-LGPA module is designed to model local fine-grained information and global contextual dependencies, improving target discrimination under complex forest environments. Additionally, a P2 tiny-object detection branch is incorporated to preserve spatial details and enhance the perception capability of tiny targets. A UAV forest fire spot detection dataset was constructed, and extensive experiments were conducted. Experimental results demonstrate that MSF-YOLO achieves a Recall of 79.27, representing an improvement of 5.09% over the baseline YOLOv8s. The mAP@0.5 and mAP@0.5:0.95 values are improved by 3.99% and 5.35%, respectively. Moreover, compared with eight improved YOLO-based small-object detectors, MSF-YOLO achieves superior overall detection performance, with a 1.35% improvement in mAP@0.5 over the best-performing comparison method. The proposed MSF-YOLO effectively addresses the challenge of early-stage tiny fire spot detection in forest fire.