[1]SHAO Yuxiao,LU Tao,WANG Zhenyu,et al.Human detection algorithm in infrared images combining multi-scale large kernel convolution[J].CAAI Transactions on Intelligent Systems,2025,20(4):787-799.[doi:10.11992/tis.202404027]
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Human detection algorithm in infrared images combining multi-scale large kernel convolution

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 787-799 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Human detection algorithm in infrared images combining multi-scale large kernel convolution
Author(s):
SHAO Yuxiao1;  LU Tao2;  WANG Zhenyu1;  PENG Yongjie1;  YAO Wei1
1. The School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
2. The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
Keywords:
infrared image; object detection; reconstruction attention; multi-scale feature; large kernel convolution; convolutional neural network; feature extraction; re-parameterization
CLC:
TP391.4
DOI:
10.11992/tis.202404027
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-parameterization 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.
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