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

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.