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<p>Addressing the challenges of small target detection difficulties and unbalanced vision in indirect vision technology for heritage building fire detection, we propose the HMIV-DET hybrid multi-scale indirect vision detection algorithm. This algorithm incorporates three key innovations: Adaptive Indirect Vision Enhancement (AIVE) resolves vision imbalance issues in multi-mirror deployments through dynamic weight allocation; Adaptive Multi-kernel Feature Orchestration Block (AMFOBlock) employs parallel multi-scale feature extraction and gated activation mechanisms to enhance the capture capability for flame features of different sizes; and Hierarchical Cross-Scale Feature Fusion Network (HCSFPN) achieves comprehensive interaction of features across multiple semantic levels. Experiments on the self-constructed Heritage Building Indirect Vision Fire Dataset demonstrate that compared to the YOLO11 baseline, mAP50 and mAP50-95 improve by 2.8% and 3.3% respectively, significantly enhancing detection performance while maintaining lightweight characteristics.</p>

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A hybrid multi-scale indirect vision detector algorithm for heritage building fire detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月26日

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  • Jian Liang,
  • Junsheng Cheng

摘要

Addressing the challenges of small target detection difficulties and unbalanced vision in indirect vision technology for heritage building fire detection, we propose the HMIV-DET hybrid multi-scale indirect vision detection algorithm. This algorithm incorporates three key innovations: Adaptive Indirect Vision Enhancement (AIVE) resolves vision imbalance issues in multi-mirror deployments through dynamic weight allocation; Adaptive Multi-kernel Feature Orchestration Block (AMFOBlock) employs parallel multi-scale feature extraction and gated activation mechanisms to enhance the capture capability for flame features of different sizes; and Hierarchical Cross-Scale Feature Fusion Network (HCSFPN) achieves comprehensive interaction of features across multiple semantic levels. Experiments on the self-constructed Heritage Building Indirect Vision Fire Dataset demonstrate that compared to the YOLO11 baseline, mAP50 and mAP50-95 improve by 2.8% and 3.3% respectively, significantly enhancing detection performance while maintaining lightweight characteristics.