Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (10): 220-225.doi: 10.11896/jsjkx.200800073

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Material Recognition Method Based on Attention Mechanism and Deep Convolutional Neural Network

XU Hua-jie1,2, YANG Yang1, LI Gui-lan3   

  1. 1 College of Computer and Electronic Information,Guangxi University,Nanning 530004,China
    2 Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China
    3 Guangxi Institute of Product Quality Inspection,Nanning 530007,China
  • Received:2020-08-11 Revised:2020-11-18 Online:2021-10-15 Published:2021-10-18
  • About author:XU Hua-jie,born in 1974,Ph.D,asso-ciate professor,is a senior member of China Computer Federation.His main research interests include artificial intelligence,acoustic signal recognition and computer vision.
  • Supported by:
    Science and Technology Plan Project of Guangxi Zhuang Autonomous Region(2017AB15008) and Science and Technology Plan Project of Chongzuo(FB2018001).

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Abstract: The purpose of material recognition is to identify the main objects and their material categories in natural material images.Aiming at the problem of low recognition accuracy caused by the lack of data in material image data sets and the difficulty of manually labeling local texture regions,a material recognition method based on attention mechanism and deep convolutional neural network is proposed.The core of the method is material recognition deep convolutional neural network (MaterialNet).MaterialNet uses the deep residual network to extract the features of the image,and introduces the attention mechanism by the proposed cascaded atrous spatial pyramid pooling method,so that the network can adaptively focus on the key areas containing texture features through end-to-end training,so as to effectively identify the local texture features of materials.Based on the FMD material datasets,the experimental results show that the overall identification accuracy of MaterialNet is 82.3%,which is 7.2％ and 4.5％ higher than the current mainstream B-CNN and CNN+FV material identification methods,respectively.The recognition accuracy of MaterialNet is high for a variety of materials,and it has the advantages of less parameters and less calculation.

Key words: Atrous convolution, Attention mechanism, Deep convolutional neural network, Spatial pyramid pooling

CLC Number: 

  • TP391
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