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An efficient fire detection algorithm based on Mamba space state linear attention

  • 0College of Information Engineering, Luoyang Polytechnic, Luoyang, China. 1018232006@tju.edu.cn.
Scientific Reports +

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April 2, 2025

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April 2, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an efficient fire detection model using the Mamba attention mechanism within the YOLOv9 architecture. The novel approach enhances object detection for dynamic environments, achieving high accuracy and computational efficiency.

Area Of Science

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background

  • Object detection models often struggle with complex scale variations and multi-view interference in dynamic environments.
  • State Space Models (SSMs) offer enhanced global receptive fields and feature extraction compared to traditional methods.
  • Existing visual fire detection technologies require performance enhancements for real-world applications.

Purpose Of The Study

  • To propose an efficient and high-performance fire detection algorithm by integrating the Mamba attention mechanism into the YOLOv9 architecture.
  • To introduce novel techniques for improving feature representation, local detail modeling, and bounding box accuracy in fire detection.
  • To provide a computationally efficient solution for visual fire detection.

Main Methods

  • Developed the Efficient Mamba Attention (EMA) module, combining adaptive average pooling with SSMs to reduce computation and enhance feature representation.
  • Integrated the ConvNeXtV2 module into the backbone network to improve the modeling of fine-grained local features.
  • Refined the loss function with a dynamic non-monotonic focusing mechanism and distance penalty strategy for better bounding box accuracy.

Main Results

  • The proposed model achieved an FPS of 71.
  • Achieved a mean Average Precision ([Formula: see text]) of 91.0% on a large-scale fire dataset and 87.2% on a small-scale fire dataset.
  • Demonstrated superior performance and significant computational efficiency compared to existing fire detection methods.

Conclusions

  • The Mamba-enhanced YOLOv9 model offers a powerful and efficient solution for visual fire detection.
  • The novel EMA module and backbone optimizations significantly improve detection accuracy and robustness.
  • This approach represents a significant advancement in real-time fire detection systems for dynamic environments.

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