ArticleSensors (Basel, Switzerland)2026
Forest Fire Detection Based on Improved YOLO11.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Research on an Improved YOLOv11-Based Detection Method for Harvestable Safflower Filaments in Unstructured Environments.Sensors (Basel, Switzerland) · 2026Article
- A Lightweight End-to-End Framework for Real-Time Vehicle-Ejected Debris Detection on Edge Devices.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
Addressing the limitations of inadequate model lightweighting and suboptimal detection accuracy in forest fire detection systems, a refined forest fire detection approach based on an improved YOLO11 architecture is proposed. Based on the YOLO11 network architecture, the backbone network is modified by integrating the ShuffleNetV1 module to achieve efficient and lightweight model deployment. Additionally, the incorporation of the SPD-Conv convolutional module not only expands the receptive field to strengthen the aggregation of semantic features for large-scale flame targets but also precisely preserves fine-grained edge and texture information of small-scale smoke targets. The experimental results show that the improved model achieves a real-time inference speed of 148.3 FPS, a 22.5% reduction in parameter count, a 0.3% improvement in mAP, and a 15.0% decrease in GFLOPs. It achieves the lightweight design of the improved YOLO11 and the improvement of detection accuracy for forest fire targets.
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Registered trials
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