Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 51-56.doi: 10.11896/jsjkx.200500122

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Image Recognition for Building Components Based on Convolutional Neural Network

XIONG Zhao-yang, WANG Ting   

  1. School of Civil Engineering,Nanchang Hangkong University,Nanchang 330063,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:XIONG Zhao-yang,born in 1996,master.His main research interests include deep learning and intelligent construction.
    WANG Ting,born in 1975,Ph.D,associate professor.Her main research interests include BIM and intelligent construction.
  • Supported by:
    National Natural Science Foundation of China (51968051).

Abstract: It is necessary to convert the point cloud data into theRGB-D images of building and classify the images,when using the point cloud data obtained by 3D laser scanner to generate BIM model for a large number of existing buildings.In this paper,based on the deep learning algorithm,a method of building components image recognition employing the convolution neural network is proposed by using the transfer learning theory to dealing with the classification problem of interior building components image such as doors and windows.First of all,the VGG16 with weight parameters trained in Imagenet is used as the image recognition neural network.In addition,the network is optimized by adding Dropout layer,L2 regularization and using Fine-tune operation to improve the recognition accuracy of the network.The experimental results show that the average recognition accuracy of the model optimized by Fine-tune is 95.4%,which is about 5.1% higher than that of the model without optimization.

Key words: Building components, Convolutional neural network, Image recognition, Transfer learning

CLC Number: 

  • TP391
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