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Museum Relic Image Detection and Recognition Based on Deep Learning.

Qi Wang1, Ling Li2

  • 1Taiyuan R & D Center, Beijing Green Rock Technology Development Co., Ltd, Taiyuan 030051, Shanxi, China.

Computational Intelligence and Neuroscience
|February 3, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances museum cultural relic image recognition using improved DenseNet and ResNet models. The enhanced ResNet achieves over 90% accuracy with the lowest detection errors, significantly improving cultural heritage identification.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Cultural Heritage Informatics

Background:

  • Accurate recognition of museum cultural relic images is crucial for cataloging and preservation.
  • Existing methods face challenges with small targets and weak feature extraction robustness.

Purpose of the Study:

  • To enhance the accuracy and robustness of museum cultural relic image recognition.
  • To address the limitations of small target detection and feature extraction in cultural relic datasets.

Main Methods:

  • Improved DenseNet with feature pyramid for multiscale feature extraction and fusion.
  • Enhanced ResNet incorporating an attention mechanism for improved feature focus.
  • Experimental validation against algorithms like YOLOv3, SVD-Net, and DenseNet.

Main Results:

  • The improved ResNet model achieved over 90% accuracy.
  • The proposed methods resulted in the lowest missed (171) and erroneous (134) detections.
  • The mean Average Precision (mAP) reached 86%, outperforming existing benchmarks.

Conclusions:

  • The developed methods effectively detect and recognize museum cultural relic images.
  • The attention mechanism in ResNet and feature pyramid in DenseNet significantly improve recognition performance.
  • This approach offers a robust solution for digital cultural heritage management.