计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 51-56.doi: 10.11896/jsjkx.200500122
熊朝阳, 王婷
XIONG Zhao-yang, WANG Ting
摘要: 对于现存的大量既有建筑,利用三维激光扫描所得到的点云数据生成BIM模型,需要将点云数据转换成建筑RGB-D图像,并对图像进行分类处理。传统图像识别技术无论是识别准确度还是面对复杂场景的模型泛化能力等,都难以满足现在的需求。文中基于深度学习算法,针对室内建筑门窗构件图像的分类问题,提出了一种运用卷积神经网络模型进行建筑构件图像识别的方法。该方法首先将收集的数据集进行数据增强处理以增加数据丰富度,并使用在ImageNet上已经训练好权重的VGG16作为识别网络,随后对网络进行优化,包括增加Dropout层、L2正则化以及采用Fine-tune操作来提升网络的识别精度。实验结果表明,进行了Fine-tune等优化后的模型的平均识别准确率达到95.4%,相比于未经过优化的模型的准确率提高了大约5.1%。
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