计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 220-225.doi: 10.11896/jsjkx.200800073
许华杰1,2, 杨洋1, 李桂兰3
XU Hua-jie1,2, YANG Yang1, LI Gui-lan3
摘要： 材质识别旨在识别自然材质图像中的主要对象及其所属材料类别。针对材质图像数据集通常数据量少、人工标注局部纹理区域困难所导致的材质识别准确率低的问题,提出了一种基于注意力机制和深度卷积神经网络的材质识别方法,该方法的核心是材质识别深度卷积神经网络(MaterialNet)。MaterialNet利用深度残差网络对图像进行特征提取,采用所提出的级联空洞空间金字塔池化的方式引入注意力机制,使网络可以通过端到端训练自适应地关注包含纹理特征的关键区域,从而有效识别材质的局部纹理特征。在FMD材质数据集上进行实验,结果表明,MaterialNet的总体识别准确率可达到82.3%,比当前主流的B-CNN和CNN+FV材质识别方法分别提高了7.2%和4.5%,对多种材质的识别准确率较高且具有参数量少、计算量小等优点。
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