基于深度学习网络的电气设备图像分类

为了对变电站中智能巡检系统采集到的海量图片进行快速分析和识别,提出一种深度学习和支持向量机(support vector machine, SVM)相结合的图像分类模型。首先,运用旋转、翻折等方法对采集到的原始数据进行扩充。然后,合并扩展图像集,并在相同类型的条件下将其随机划分为训练集和测试集。基于实际图像改进卷积神经网络(convolutional neural network, CNN),并提取训练集的图像特征。最后,通过使用训练集图片的深度特征来训练SVM分类器,并且在测试集图片上实现分类测试。利用巡检机器人采集到的8 000张图片对模型精度进行实验验证,结果表明,该模型具有较强的分类性能。

Electrical Equipment Image Classification Based on Deep Learning Network

In order to quickly analyze and identify the massive images collected by the intelligent inspection system in the substation, an image classification model combining deep learning and support vector machine(SVM) is proposed. First, the original data collected was expanded by means of rotation, folding, etc. The expanded images were then randomly divided into training sets and test sets according to the types, and the parameters in the convolutional neural network (CNN) were improved by actual data, and the training set image features were extracted on the trained CNN models. Finally, the SVM classifier was trained by using the depth features of the training set image, and the test set image was classified and verified. The experiment uses 8000 pictures collected by the inspection robot in a substation to verify the accuracy of the model. The results show that the model has strong classification performance.