红外技术2026，Vol.48Issue(8)：969-977,9.
基于改进YOLOv7的多场景火灾识别算法
Multi-Scenario Fire Detection Algorithm Based on Improved YOLOv7
摘要
Abstract
To address the issue of reduced detection efficiency in traditional fire alarm systems owing to environmental and spatial limitations during fire incidents,this study investigated an improved YOLOv7 algorithm specifically designed for multi-environment fire detection.A dynamic head was integrated into the original algorithm to enhance the detection accuracy,and a global attention mechanism and slim neck model structure were introduced to improve the extraction of fire-related features and reduce the computational complexity.Additionally,the Wise-IoUv3 loss function was employed instead of the traditional IoU loss function in the model,and the training weights were adjusted based on the image quality to optimize the model efficiency.In an experiment using a multi-environment fire dataset,the improved YOLOv7 algorithm achieved an mAP@0.5 of 71.1%,which was a 5.3 percent improvement over the result with the original algorithm.This demonstrated a high level of detection accuracy and effectively met the requirements for precise fire detection in complex environments.关键词
火灾识别/YOLOv7/目标检测/注意力机制/Slim-NeckKey words
fire detection/YOLOv7/detection head/attention mechanism/Slim-Neck分类
资源环境引用本文复制引用
杨思远,王冠凌,江自豪,王坤相,王世康,何宇豪..基于改进YOLOv7的多场景火灾识别算法[J].红外技术,2026,48(8):969-977,9.基金项目
国家自然科学基金(U22A2079) （U22A2079）
安徽高校自然科学研究重大项目(J2021ZD0116) （J2021ZD0116）
皖江高端装备制造协同创新中心开放基金项目(GCKJ2018013). （GCKJ2018013）
