摘要
为提高UAV红外图像语义分割的性能,提出基于深度学习和高斯伯努利受限玻尔兹曼机(GB-RBM)的实时语义分割模型。确认地面车辆实时特征提取中的关键问题。基于GB-RBM,提出用于编码阶段的形状先验模型。通过将SegNet中的编码器-解码器结构与GB-RBM模块相融合,在解码器块中生成红外数据的实时映射,实现准确快速的语义分割。实验结果表明,所提方法能够很好地处理红外视频中的实时几何信息,在3个实验数据集上的平均精度约为0.98,平均处理时长约为17.86 s,性能优于其它优秀方法。
To improve the performance of UAV infrared image semantic segmentation,a real-time semantic segmentation model based on deep learning and Gaussian-Bernoulli restricted Boltzmann machine(GB-RBM)was proposed.The key issues related to real-time feature extraction of ground vehicles were identified.A GB-RBM model based shape prior model for encoding block was proposed.The real-time mapping of the thermal data in the decoder block was realized through the fusion of encoder-decoder structure of the Segnet architecture and the GB-RBM module,eventually leading to accurate and fast semantic segmentation.Experimental results show that the proposed method can properly process the real-time geometric information in the infrared vi-deos.The mean precision and the average processing time on all datasets when using the proposed method are 0.98 and 17.86 s,respectively,which outperform that using other state-of-art methods.
作者
冯向东
邬忠萍
郝宗波
FENG Xiang-dong;WU Zhong-ping;HAO Zong-bo(Department of Basic Teaching,The Engineering&Technical College of Chengdu University of Technology,Leshan 614000,China;School of Automobile and Transportation,Chengdu Technological University,Chengdu 611730,China;School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China)
出处
《计算机工程与设计》
北大核心
2023年第8期2432-2438,共7页
Computer Engineering and Design
基金
国家自然科学基金项目(61003032)
四川省重点实验室开放课题基金项目(2020YW003、scsxdz2019by01)
2019年度乐山市重点科技计划基金项目(19GZD051)。
关键词
深度学习
语义分割
受限玻尔兹曼机
红外图像
编码器-解码器
特征提取
几何信息
deep learning
semantic segmentation
restricted Boltzmann machine
infrared image
encoder-decoder
feature extraction
geometric information