Dense bone stick text detection algorithm in complex texture background

LI Jian-yu; WANG Hui-qin; LIU Rui; WANG Ke; WANG Zhan

doi:10.37188/CJLCD.2022-0393

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Dense bone stick text detection algorithm in complex texture background

Image Processing | 更新时间：2023-09-27

- Dense bone stick text detection algorithm in complex texture background
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 9, Pages: 1293-1303(2023)
- 作者机构：
  
  1.西安建筑科技大学信息与控制工程学院，陕西西安 710055
  2.中国社会科学院考古研究所，北京 100101
  3.陕西省文物保护研究院，陕西西安 710075
- 作者简介：
- 基金信息：
  
  National Social Science foundation for rare and unique studies project of China(20VJXT001)
- DOI：10.37188/CJLCD.2022-0393
  CLC：
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LI Jian-yu, WANG Hui-qin, LIU Rui, et al. Dense bone stick text detection algorithm in complex texture background. [J]. Chinese Journal of Liquid Crystals and Displays 38(9):1293-1303(2023)
DOI：

LI Jian-yu, WANG Hui-qin, LIU Rui, et al. Dense bone stick text detection algorithm in complex texture background. [J]. Chinese Journal of Liquid Crystals and Displays 38(9):1293-1303(2023) DOI： 10.37188/CJLCD.2022-0393.

摘要

骨签是记载西汉时期地方工官向中央上缴产品的重要文物，准确检测其文字内容具有重要意义。针对复杂纹理背景下骨签文字特征难以提取及文字密集、粘连导致检测框冗余的问题，提出融合自注意力卷积和改进损失函数的骨签文字检测算法。首先，在YOLOv5特征提取端加入自注意力卷积模块，增强网络对骨签文字特征的注意，同时使模型捕捉更丰富的全局信息，抑制裂痕对文字特征提取的干扰。其次，使用Focal-EIOU损失函数替换原网络的CIOU进行优化，Focal-EIOU使用宽高损失降低预测框与真实框的宽高差距，剔除大于真实框的预测框，解决文字密集与粘连产生的检测框冗余问题，进而提高模型精准预测能力。实验结果表明，本文算法的平均精确率达到93.35%，相比YOLOv5提高了3.08%，对于复杂纹理背景下的密集粘连骨签文字检测任务更为适用。

Abstract

Bone stick is an important cultural relic that records the products handed over by local officials to the central government in the Western Han Dynasty. It is of great significance to accurately detect the written content. In order to solve the problem that bone stick text is difficult to extract under complex texture background and the dense text and adhesion lead to multiple characters in one frame， a bone stick text detection algorithm combining self-attention convolution and improved loss function is proposed. Firstly， a self-attention convolution module is added to the YOLOv5 feature extraction to enhance the network's attention to the features of bone stick， and to make the model capture more global information and suppress the interference of the crack to the feature extraction. In addition， the Focal-EIOU loss function is used to replace the CIOU network for optimization. Focal-EIOU uses the wide-height loss to reduce the wide-height gap between the prediction box and the real box， and eliminates the prediction box larger than the real box， the detection frame redundancy problem caused by text density and adhesion is solved to improve the precision prediction ability of the model. The experimental results show that the average accuracy of the proposed algorithm reaches 93.35%， which is 3.08% higher than that of YOLOv5. It is more suitable for the task of detecting dense adhesive bone stick text in complex texture background.

关键词

文字检测骨签注意力机制YOLOv5损失函数

Keywords

text detectionbone stickattention mechanismYOLOv5loss function

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