Xu Zhendong, Zhao Hongdan, Zheng Yu, Guo Hongbo, Li Shengyang, Lyu Zhiyu
State Grib Jilin Electric Power Co., Ltd, Liaoyuan Power Supply Company, Liaoyuan, China.
School of Automation Engineering, Northeast Electric Power University, Jilin, China.
PeerJ Comput Sci. 2024 Jan 26;10:e1817. doi: 10.7717/peerj-cs.1817. eCollection 2024.
The most direct way to find the electrical switchgear fault is to use infrared thermal imaging technology for temperature measurement. However, infrared thermal imaging images are usually polluted by noise, and there are problems such as low contrast and blurred edges. To solve these problems, this article proposes a dual convolutional neural network model based on nonsubsampled contourlet transform (NSCT). First, the overall structure of the model is made wider by combining the two networks. Compared with the deeper convolutional neural network, the dual convolutional neural network (CNN) improves the denoising performance without increasing the computational cost too much. Secondly, the model uses NSCT and inverse NSCT to obtain more texture information and avoid the gridding effect. It achieves a good balance between noise reduction performance and detail retention. A large number of simulation experiments show that the model has the ability to deal with synthetic noise and real noise, which has high practical value.
查找电气开关设备故障最直接的方法是使用红外热成像技术进行温度测量。然而，红外热成像图像通常会受到噪声污染，并且存在对比度低和边缘模糊等问题。为了解决这些问题，本文提出了一种基于非下采样轮廓波变换（NSCT）的双卷积神经网络模型。首先，通过将两个网络相结合使模型的整体结构更宽。与更深的卷积神经网络相比，双卷积神经网络（CNN）在不过多增加计算成本的情况下提高了去噪性能。其次，该模型使用NSCT和逆NSCT来获取更多纹理信息并避免网格效应。它在降噪性能和细节保留之间实现了良好的平衡。大量仿真实验表明，该模型具有处理合成噪声和真实噪声的能力，具有较高的实用价值。
