微型电脑应用

为了提高智能识别效果，设计一种基于无人机的架空输电线路缺陷智能识别方法。建立图像信息提取模型，在图像信息卷积输出结构中获取卷积核的输入输出值以及激活函数，得到函数的回归损失函数值。生成架空输电线路缺陷判别网络，得到不同采样点的目标函数，建立离散微分算子的矩阵信息，计算双边拟合误差，匹配特征点的函数值。设计线路缺陷智能识别算法，完成架空输电线路缺陷的智能识别方法设计。实验结果证明，设计方法可以以最少的训练次数获取最小的损失值，得到损失值的最小值约为0.35,识别效果更佳，具有一定应用价值。

In order to improve the effect of intelligent recognition, this paper presents an intelligent recognition method of overhead transmission line defects based on UAV. The image information extraction model is established, and the input and output values of the convolution kernel and the activation function are obtained in the image information convolution output structure, and the regression loss function value of the function is also obtained. The overhead transmission line defect identification network is generated to obtain the objective function of different sampling points, establish the matrix information of discrete differential operator, calculate the bilateral fitting error, and match the function value of the feature points. The intelligent identification algorithm of line defects is designed to complete the design of intelligent identification method of overhead transmission line defects. The experimental results show that the design method can obtain the minimum loss value with the least training times, and the minimum loss value is about 0.35. The recognition effect is better, and it has certain application value.