红外技术2026，Vol.48Issue(2)：176-183,8.
基于改进DeepLabv3+的电力设备红外图像分割算法
Infrared Image Segmentation Algorithm of Power Equipments Based on Improved DeepLabv3+Algorithm
摘要
Abstract
This study aims to address the problems of low accuracy and long processing times of infrared image segmentation of power equipment in complex backgrounds.In this study,an infrared image segmentation algorithm based on the improved DeepLabv3+algorithm is proposed for power equipment.First,lightweight CA-MobileNetV3 was used instead of Xception to realize feature extraction,reduce model parameters,and improve segmentation accuracy.Second,atrous spatial pyramid pooling(ASPP)was replaced with spatial pyramid(SP)-Dense ASPP to extract a denser and wider range of detailed features and enhance the strip characteristics.Finally,the efficient channel attention(ECA)mechanism was introduced to realize the effective fusion of different levels of feature information and improve segmentation accuracy and robustness of the model.The experimental results showed that the proposed algorithm had higher feasibility and effectiveness in the actual infrared image segmentation task of power equipment than the four more advanced semantic segmentation models.The average increase in mean pixel accuracy(MPA)was 2.67%,and the average increase in mean intersection over union(mIoU)was 9.32%.关键词
图像分割/红外图像/注意力机制/DeepLabv3+Key words
image segmentation/infrared image/attention mechanism/DeepLabv3+分类
信息技术与安全科学引用本文复制引用
邓长征,骆冰洁,付添,弓萌庆,刘明泽..基于改进DeepLabv3+的电力设备红外图像分割算法[J].红外技术,2026,48(2):176-183,8.基金项目
湖北省教育厅科学技术研究计划中青年人才项目(Q20151205). （Q20151205）
