2025 01 v.55 36-49
面向甲骨文目标检测的大规模数据集生成技术
基金项目(Foundation):
古文字与中华文明传承发展工程规划项目(G1811);
国家语委科研项目(WT145-9)
邮箱(Email):
lihuabiao@chnmuseum.cn;
DOI:
10.16152/j.cnki.xdxbzr.2025-01-003
中文作者单位:
中国国家博物馆;馆藏资源活化技术文化和旅游部重点实验室;
摘要(Abstract):
甲骨文目标检测是甲骨文数字化研究中重要一环，主要依靠深度学习模型实现对甲骨文图像中位置信息和分类信息的识别。为了避免模型过拟合，深度学习模型的训练一般需要依赖大规模的数据集，而在甲骨文目标检测领域，目前可用于深度学习的大规模数据集较少，很多研究所用的数据集均依靠专家人工标注和整理，这使得甲骨文目标检测数据集存在整理成本较高、数据量较小、数据质量不高、类别间均衡性差等问题。提出了动态两阶段Mosaic算法及甲骨文大规模数据集生成技术，解决传统Mosaic算法在处理甲骨文图像中存在的拼接图数量有限、图像的多样性和差异性不足、空白背景较大、信息缺失等问题，并设计了完整的数据集生成流程，实现了从甲骨文单字符图片到大规模数据集生成的流程化、智能化处理，从根本上解决了甲骨文目标检测领域的数据困境。通过此研究方法，生成了标注位置信息和类别信息且规模庞大的甲骨文数据集，共生成57万张甲骨文图像和57万份对应的标注文件，包含甲骨文类别416类，样本数量最少的类别包含了516个甲骨文字符，且数据集规模和各类别样本数量可动态调整以避免类别间样本不均衡。采用YOLOv8模型对生成后的大规模数据集进行训练，在经过200批次训练后，模型精度(Precision)达到96.45%,mAP50值为97.75%,mAP50-95值为96.96%,从模型训练曲线看，训练过程表现出较好的稳定性和高效性，模型训练结果表明，研究的数据集生成技术可应用于甲骨文目标检测。
关键词(KeyWords):
甲骨文;深度学习;目标检测;数据集;YOLOv8算法
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[21] GUO J,WANG C H,ROMAN-RANGEL E,et al.Building hierarchical representations for oracle character and sketch recognition[J].IEEE Transactions on Image Processing,2016,25(1):104-118.
[22] IZUMI T,MENG L.A combined recognition system for oracle bone inscriptions[J].International Journal of Advanced Mechatronic Systems,2017,7(4):235.
[23] 台湾历史语言研究所.甲骨文數位典藏資料庫[DB/OL].(2020-07-22)[2023-08-22].https://rub.ihp.sinica.edu.tw/～oracle/main4.htm.
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[26] 孟宪佳，傅利平，刘栋，等.高性能计算发展现状及其在文化遗产保护中的应用展望[J].西北大学学报(自然科学版),2021,51(5):807-815.MENG X J,FU L P,LIU D,et al.Development status of high performance computing and its application prospect in cultural heritage protection[J].Journal of Northwest University (Natural Science Edition),2021,51(5):80
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[28] ZHANG C Y,BENGIO S,HARDT M,et al.Understanding deep learning (still) requires rethinking generalization[J].Communications of the ACM,2021,64(3):107-115.
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[44] YUN S,HAN D,OLF S J,et al.CutMix:Regularization strategy to train strong classifiers with localizable features[EB/OL].(2019-08-07)[2024-08-08].http://arxiv.org/abs/1905.04899.
[45] BOCHKOVSKIY A,WANG C Y,LIAO H M.YOLOv4:Optimal speed and accuracy of object detection[EB/OL].(2020-04-23)[2024-08-08].http://arxiv.org/abs/2004.10934.
[46] IOFFE S,SZEGEDY C,PARANHOS L,et al.Batch normalization:Accelerating deep network training by reducing internal covariate shift[EB/OL].(2015-03-02)[2024-08-08].http://arxiv.org/abs/1502.03167.
[47] SHAHINFAR S,MEEK P,FALZON G."How many images do I need?" Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring[J].Ecological Informatics,2020,57:101085.
[48] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:Unified,real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).June 27-30,2016.Las Vegas,NV,USA:IEEE,2016:779-788.
[49] JOCKER G,CHAURASIA A,QIU J.Ultralytics YOLO (Version 8.0.0)[CP/OL].(2023-07-20)[2024-03-17].https://github.com/ultralytics/ultralytics.
基本信息:
DOI：10.16152/j.cnki.xdxbzr.2025-01-003
中图分类号:TP391.41;TP18;K877.1
引用信息:
[1]杨富勇,李华飙,孟睿伟.面向甲骨文目标检测的大规模数据集生成技术[J].西北大学学报(自然科学版),2025,55(01):36-49.DOI:10.16152/j.cnki.xdxbzr.2025-01-003.
基金信息:
古文字与中华文明传承发展工程规划项目(G1811); 国家语委科研项目(WT145-9)