2025 01 v.55 98-105
人工智能目标检测技术在书画文物病害调查中的应用
基金项目(Foundation):
国家档案局科技项目计划(2021-B-03、2022-B-002)
邮箱(Email):
yczhang@ynu.edu.cn;
DOI:
10.16152/j.cnki.xdxbzr.2025-01-008
中文作者单位:
云南大学历史与档案学院;云南大学软件学院;云南省博物馆;
摘要(Abstract):
针对书画文物保护工作中人工病害调查和病害图绘制效率低的问题，探索了基于深度神经网络的目标检测技术识别书画病害的可行性。选择YOLOv5系列模型并根据本研究任务特点对其结构做了优化，包括FGSM算法、CmBN策略、Dropblock正则化和CIOU_-Loss损失函数。利用博物馆馆藏书画文物素材，融合Mosaic数据增强方法进行书画文物图片的增强，设计了滑动窗口检测技术、图像逐层分析和定位裁剪技术，初步训练出了2个具备病害识别功能的模型，根据模型性能检验指标最终选择了YOLOv5x6作为本研究任务的模型。测试结果表明，该模型以较高的准确率和查全率识别出了待检测病害，用时仅为人工的千分之一。该技术的引入可极大提高文物病害识别效率，并且在病害识别过程中保持客观、稳定的标准。
关键词(KeyWords):
病害识别;目标检测;图像处理;YOLOv5;深度神经网络
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参考文献
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[2] 田朋，尚小临，王若苏.古书画保护修复中的二次脱色现象及预防[J].文物保护与考古科学，2022,34(1):35-41.TIAN P,SHANG X L,WANG R S.Research on the secondary shedding of pigment in the restoration of ancient paintings and calligraphy and its prevention[J].Sciences of Conservation and Archaeology,2022,34(1):35-41.
[3] 王婕，巨建伟，王天凤，等.唐卡病害图绘制的规范化研究：以《威罗瓦金刚画像轴》病害图的绘制为例[J].中国文物科学研究，2020(4):81-88.WANG J,JU J W,WANG T F,et al.A study on the drawing Tangka disease maps:Taking the drawing of disease maps in the "Verois Vajra Thangka " as an example [J].China Cultural Heritage Scientific Research,2020(4):81-88.
[4] 国家文物局.馆藏纸质文物病害分类与图示：WWT 0026-2010 [S].北京：中国标准出版社，2010.
[5] 谷明岩.基于改进U-Net的壁画颜料层脱落病害提取研究[D].北京：北京建筑大学，2020.
[6] 刘晓琴，侯妙乐，董友强，等.基于高光谱影像的瞿昙寺壁画颜料层脱落病害评估[J].地理信息世界，2019,26(5):22-28.LIU X Q,HOU M L,DONG Y Q,et al.Extraction and evaluation of the disease of the mural paint Loss[J].Geomatics World,2019,26(5):22-28.
[7] 胡春梅，王瑜，黄浩雯，等.基于纹理的文物病害高精度自动提取方法：CN110796181B[P].2022-05-03.
[8] 曹建芳，李艳飞，崔红艳，等.改进的区域生长算法在寺观壁画脱落病害标定中的应用[J].新疆大学学报(自然科学版),2018,35(4):429-436.CAO J F,LI Y F,CUI H Y,et al.The application of improved region growing algorithm for the automatic calibration of shedding disease on temple murals[J].Journal of Xinjiang University(Natural Science Edition),2018,35(4):429-436.
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[10] 王珺，孙进越，俞凯，等.基于分组LSTM与CNN的青铜器锈蚀类别智能标识方法[J].西北大学学报(自然科学版),2021,51(5):778-786.WANG J,SUN J Y,YU K,et al.Intelligent identification method of bronze rust category based on grouping LSTM and CNN[J].Journal of Northwest University (Natural Science Edition),2021,51(5):778-786.
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[12] 李彩艳，王慧琴，吴萌，等.唐墓室壁画泥斑病害自动标定及虚拟修复[J].计算机工程与应用，2016,52(15):233-236.LI C Y,WANG H Q,WU M,et al.Automatic recognition and virtual restoration of mud spot disease of Tang dynasty tomb murals image[J].Computer Engineering and Applications,2016,52(15):233-236.
[13] 姜军，卓嘎，王朝霞.西藏数字壁画泥斑病害自动标定修复方法仿真[J].计算机仿真，2018,35(11):215-219.JIANG J,ZHUO G,WANG Z X.Digital curtain diameter mould disease auto calibration and restoration method simulation[J].Computer Simulation,2018,35(11):215-219.
[14] 谢富，朱定局.深度学习目标检测方法综述[J].计算机系统应用，2022,31(2):1-12.XIE F,ZHU D J.Survey on deep learning object detection[J].Computer Systems & Applications,2022,31(2):1-12.
[15] 谭营.人工智能知识讲座[M].北京：人民出版社，2018.
[16] NARESH K,SATISH G N,RAM K B,et al.Machine learning based recognition of crops diseases by CNN[J].International Journal of Innovative Technology and Exploring Engineering,2019,8(9):1264-1268.
[17] 刘学增，桑运龙，苏云帆.基于数字图像处理的隧道渗漏水病害检测技术[J].岩石力学与工程学报，2012,31(S2):3779-3786.LIU X Z,SANG Y L,SU Y F.Detection technology of tunnel leakage disaster based on digital image processing[J].Chinese Journal of Rock Mechanics and Engineering,2012,31(S2):3779-3786.
[18] XIANG K,PENG L L,YANG H Q,et al.A novel weight pruning strategy for light weight neural networks with application to the diagnosis of skin disease[J].Applied Soft Computing,2021,111:107707.
[19] ZHENG Z H,WANG P,LIU J W,et al.Distance-IoU loss:Faster and better learning for bounding box regression[J].Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(7):12993-13000.
[20] 冯增木.中国书画装裱[M].3版.济南：山东科学技术出版社，2002:207.
基本信息:
DOI：10.16152/j.cnki.xdxbzr.2025-01-008
中图分类号:K879.4;TP391.41;TP18
引用信息:
[1]邓旭帅,李子璇,张云春等.人工智能目标检测技术在书画文物病害调查中的应用[J].西北大学学报(自然科学版),2025,55(01):98-105.DOI:10.16152/j.cnki.xdxbzr.2025-01-008.
基金信息:
国家档案局科技项目计划(2021-B-03、2022-B-002)