南京师大学报（自然科学版）2025，Vol.48Issue(4)：87-95,105,10.DOI:10.3969/j.issn.1001-4616.2025.04.009
增强感受野特征的多尺度火灾检测方法
Multi-Scale Flame Detection Based on Enhanced Receptive Field Feature
摘要
Abstract
Aiming at the problems of poor fire detection effect and weak anti-interference ability,a multi-scale fire detection method based on enhanced receptive field feature is proposed.Firstly,the Receptive-Field Attention Convolution(RFAConv)is introduced to enhance the extraction of spatial features of receptive field.Secondly,the C2fiC module is designed by combining the Inverted Residual Mobile Block(iRMB)and the Channel Prior Convolutional Attention(CPCA)mechanism to improve the ability of the model to express and fuse different scale features.Then,the shared parameter structure is adopted,and the lightweight convolution reconstruction detector is introduced to reduce the model parameters and computational complexity.Finally,the Focaler-GIoU loss function is introduced to balance the difficulty samples.The experimental results show that the number of parameters and the amount of calculation of the improved model are reduced,and the detection accuracy is higher,which can meet the detection requirements in flame detection.关键词
火灾检测/感受野特征/注意力机制/损失函数/YOLOv8nKey words
flame detection/receptive field feature/attention mechanism/loss function/YOLOv8n分类
信息技术与安全科学引用本文复制引用
董可,严云洋,耿嘉雯,于永涛,王盘龙,叶翔..增强感受野特征的多尺度火灾检测方法[J].南京师大学报（自然科学版）,2025,48(4):87-95,105,10.基金项目
国家自然科学基金资助项目(62076107)、江苏省"六大人才高峰"资助项目(2013DZXX-023). （62076107）
