基于卷积神经网络的设备安装位置智能识别方法

该文针对IT系统集成机柜建立了一套基于卷积神经网络的柜内设备智能识别系统。设计基于图像处理和深度学习的识别算法以实现机柜内设备安装位置的检测识别。建立设备图像数据集对YOLO模型进行训练,得到合适的加权系数,使模型损失函数值最小;将训练好的模型对实际环境中的机柜进行检测并与其他经典方法进行对比。实验结果显示,该系统检测的准确率和实时性都优于其他算法,准确率最高达到93.9%。对于建立基于机器视觉的设备识别和控制系统有重要意义,可以在一定程度是上取代人工识别和操作。

Intelligent identification method of equipment installation position based on convolution neural network

In this paper,a set of intelligent identification system based on convolution neural network is established for system integrated cabinet,and a recognition algorithm based on image processing and deep learning is designed to detect and identify the installation position of equipment in the cabinet.The equipment image data set is established to train the YOLO model,and the appropriate weighting coefficient is obtained to minimize the loss function value of the model.The trained model is used to detect the cabinet in the actual environment and compared with other classical methods.The experimental results show that the detection accuracy and real-time performance of the system are better than other algorithms.The highest accuracy is 93.9%.It is of great significance for the establishment of equipment identification and control system based on machine vision,which can replace manual identification and operation to a certain extent.