基于数据增强的秦俑碎片深度分类模型  

Data Enhanced Depth Classification Model for Terracotta Warriors Fragments

作　　者：鱼跃华 张海波[1] 李昕 寇姣姣 李康[1] 耿国华[1] 周明全[1] Yu Yuehua;Zhang Haibo;Li Xin;Kou Jiaojiao;Li Kang;Geng Guohua;Zhou Mingquan(College of Information Science and Technology,Northwest University,Xi’an 710127,Shaanxi.China)

机构地区：[1]西北大学信息科学与技术学院,陕西西安710127

出　　处：《激光与光电子学进展》2022年第18期101-110,共10页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2019YFC1521103,2019YFC1521102);国家自然科学基金青年基金(61902317);国家自然科学基金重点项目(61731015);陕西省重点产业链项目(2019ZDLSF07-02);陕西省自然科学基金青年基金(2019JQ-166);青海省重点研发计划(2020-SF142)。

摘　　要：在秦俑保护领域,为了降低秦俑碎片匹配及拼接的工作难度,更多的计算机辅助技术应用在破碎秦俑复原工作核心环节的碎片分类中。针对传统的秦俑碎片分类方法对碎片特征提取不充分及秦俑碎片数据采集难度较高等导致的分类准确率低下的问题,提出了一种基于数据增强的秦俑碎片深度分类模型。首先,通过条件生成式对抗网络对现有秦俑碎片数据集进行数据增强,实现秦俑数据集的扩充。其次,通过深度卷积神经网络自动且充分地提取碎片特征信息并实现有效的碎片分类效果。然后,引入convolutional block attention module(CBAM)双通道注意力机制和CutMix增强策略来显著提升深度分类模型的性能。最后,在秦俑实验数据集的对比实验结果表明,与传统的基于几何特征、尺度不变特征变换特征、形状特征、多特征融合等经典碎片分类方法相比,所提方法对秦俑碎片的分类取得了更准确的分类结果,有效降低了后续复原工作中匹配、拼接等工作的复杂度,进而提高了秦俑文物复原工作的整体效率。In the field of Terracotta Warriors protection,to reduce the challenge of matching and splicing fragments of the Terracotta Warriors,more computeraided technology is applied to the core link’s debris classification in the restoration of the broken Terracotta Warriors.The classification accuracy is low because of insufficient characteristic extraction of traditional Terracotta Warriors debris classification approaches and increased difficulty associated with data collection.In this paper,a depth classification model of Terracotta Warriors fragments based on data enhancement is presented.First,the existing dataset of Terracotta Warriors fragments was improved using conditional generative adversarial nets to achieve the dataset’s expansion of Terracotta Warriors.Second,the deep convolutional neural network was employed to automatically and effectively extract the debris feature information and achieve an effective debris classification effect.Third,the doublechannel attention mechanism of the convolutional block attention module(CBAM)and the CutMix enhancement strategy were effectively introduced to significantly improve the deep classification model’s performance.Results on the experimental dataset of the Terracotta Warriors reveal that the presented approach is more accurate than the traditional classical debris classification approaches based on geometric,scaleinvariant feature transform,and shape features as well as the multifeature fusion.It can effectively reduce the subsequent restoration work’s complexity,such as matching and stitching,and therefore improve the overall efficiency of the Terracotta Warriors’restoration work.

关 键 词：图像处理 破碎秦俑复原 碎片分类 条件生成式对抗网络 双通道注意力机制 增强策略 

分 类 号：TP391.[自动化与计算机技术—计算机应用技术]