Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 231000034-6.doi: 10.11896/jsjkx.231000034

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Dunhuang Mural Element Detection Algorithm Based on Improved Yolov8

ZHOU Yanlin1,2, WU Kaijun1, MEI Yuan1, TIAN Bin1, YU Tianxiu2   

  1. 1 Lanzhou Jiaotong University,Lanzhou 730070,China
    2 Dunhuang Academy,Dunhuang,Gansu 736200,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:ZHOU Yanlin,born in 1989,bachelor degree,librarian.His main research interests include cultural relics digitization,and deep learning and computer vision.
  • Supported by:
    Natural Science Foundation of Gansu Province(23JRRA913).

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Abstract: The Dunhuang murals have garnered significant attention for their artistic,historical,and research value.In the research and development of cultural tourism surrounding frescoes,detecting elements within these frescoes is crucial.However,due to factors such as shedding,pigment fading,pest damage,and the significant discrepancies in elemental volume,detecting mural elements has become difficult.For this reason,this paper,which is based on the Yolov8 algorithm,continues the improvement and expansion work by introducing it into the fresco element detection task.Specifically,the design of an enhanced SPPCSPC module improves the feature-perception ability of the model and expands its sensory field.Additionally,the CoordAttention mechanism is introduced at the end of the C2f module to improve the network's ability to focus on local and non-significant information,which addresses the variability in volume and style of the elements.On the issue of detecting elements within Dunhuang murals,our algorithm outperforms five other cutting-edge detection algorithms in terms of mural detection accuracy.Compared to the Yolov8 baseline algorithm,it achieves a 2.2% improvement in mAP,particularly in the main_buddha category where we see a 12.2% improvement in detection accuracy.This accomplishment offers significant support for future research focused on Dunhuang murals analysis.

Key words: Dunhuang murals, Improved Yolov8, Target detection, Feature enhancement

CLC Number: 

  • TP399
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