基于神经棒的电力变压器离线图像识别研究与应用

Research and application of off-line image recognition of power transformer based on neural rod

为解决智能移动设备在电力变压器等设备的巡检、运维工作的图像识别中难以适用于无网络的离线环境问题,本文综合考虑电力设备巡检、运维工作的实际需求和真实情况,设计一种基于神经棒的电力变压器离线图像识别系统。首先,引入卷积神经网络算法,构建深度学习图像识别模型,并利用真实图片数据集对模型进行训练调优;然后,创新地将模型集成到神经计算加速棒中,搭建电力变压器离线图像识别系统,进一步解决网络对系统的束缚;最后将本文的基于神经棒的电力变压器离线图像识别系统与现有的4G网环境下移动设备识别系统、离线环境下移动设备识别系统在贵州电网贵阳局城北分局提供的真实图片数据集上进行对比验证,结果表明本文提出的基于神经棒的电力变压器离线图像识别系统具有较高的识别准确率和稳定性。

In order to solve the problem that intelligent mobile devices are difficult to be applied to the off-line environment without network in the image recognition of inspection and operation and maintenance of power transformer and other equipment,this paper designs an off-line image recognition system of power transformer based on neural rod,considering the actual needs and real situation of inspection and operation and maintenance of power equipment.First,the convolution neural network algorithm is introduced to build the deep learning image recognition model,and the real image data set is used to train and optimize the model.then,the model is innovatively integrated into the neural computing accelerator,and the off-line image recognition system of power transformer is built to further solve the constraints of the network on the system.finally,the off-line image recognition system of power transformer based on neural rod is compared with the existing mobile device recognition system in 4G network environment and the mobile device recognition system in off-line environment on the real picture data set provided by Chengbei branch of Guiyang power supply bureau.The results show that the off-line image recognition system of power transformer based on neural rod proposed in this paper has High recognition accuracy and stability.