A hybrid multi-scale indirect vision detector algorithm for heritage building fire detection
Year: 2025
Type: article
Abstract: 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.
Source: Scientific Reports
Authors: Jian Liang, Junsheng Cheng
Institution: Hunan University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7117309779