智能系统学报2025，Vol.20Issue(4)：787-799,13.DOI:10.11992/tis.202404027
结合多尺度大核卷积的红外图像人体检测算法
Human detection algorithm in infrared images combining multi-scale large kernel convolution
邵煜潇 1鲁涛 2王震宇 1彭勇杰 1姚巍1
作者信息
- 1. 华北电力大学控制与计算机工程学院,北京 102206
- 2. 中国科学院自动化研究所多模态人工智能系统全国重点实验室,北京 100190
- 折叠
摘要
Abstract
Aiming at the problems of low image resolution and inconspicuous human features in the human detection task of infrared images under the ruins environment,an infrared image human detection network re-parameterization multi-scale large kernel convolution(RML-YOLO)is designed based on the YOLO framework,which includes re-para-meterization and multi-scale large kernel convolution.The network,RML-YOLO,reconfigures the spatial and channel reconstruction attention module to focus on regions that are more important for the detection task.Edge features are strengthened by the Sobel operator to improve the detection ability of human with different poses.The validity of RML-YOLO is verified on a homegrown dataset.With only 1.8× 106 learnable parameters,the AP50 and AP50-75 of the model reach 91.2％and 87.3％,respectively,which are improved by 4.4％and 5.3％compared with YOLOv8-n with similar number of parameters.The results show that RML-YOLO significantly improves the accuracy of human detection in the ruins environment using infrared images.关键词
红外图像/目标检测/重构注意力/多尺度特征/大核卷积/卷积神经网络/特征提取/重参数化Key words
infrared image/object detection/reconstruction attention/multi-scale feature/large kernel convolution/con-volutional neural network/feature extraction/re-parameterization分类
信息技术与安全科学引用本文复制引用
邵煜潇,鲁涛,王震宇,彭勇杰,姚巍..结合多尺度大核卷积的红外图像人体检测算法[J].智能系统学报,2025,20(4):787-799,13.
