基于深度学习的电力图像分割算法研究与优化

Research and optimization of electric power image segmentation algorithm based on deep learning

电力图像分割在电力系统中具有重要的应用价值,可以提高电力设备的检测和故障诊断效果,但传统的电力图像分割算法存在准确度低、处理时间长等问题。对此,本文提出了基于深度学习的电力图像分割算法并对其进行了适当优化,将卷积神经网络(CNN)作为基本的深度学习模型,提取了电力图像中的特征。实验结果表明,该算法在准确度和处理速度方面具有显著优势,可以为电力系统的检测和故障诊断提供有效支持。

Power image segmentation has important application value in the power system,which can improve the detection and fault diagnosis of power equipment.However,traditional power image segmentation algorithms have problems such as low accuracy and long processing time.In response to this,this article proposes a deep learning based power image segmentation algorithm and optimizes it appropriately,using Convolutional Neural Networks(CNN)as the basic deep learning model to extract features from power images.The experimental results show that the algorithm has significant advantages in accuracy and processing speed,and can provide effective support for the detection and fault diagnosis of power systems.