小说双卷积自适应焦点神经网络书籍类型分类类型分类
Qingtao Zeng1, Lixin Zhang1, Jiefeng Zhao1
1Beijing Institute of Graphic Communication, Beijing, China.
PloS one
|November 7, 2025
概括
一个新的CPPDE-YOLO模型通过使用双卷积和多尺度注意力来提高书籍封面分类的准确性. 这种深度学习方法改进了YOLOv8,以有效地识别书籍的类型.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习 机器学习
背景情况:
- 书封面分析对于管理大型藏品至关重要.
- 传统的手工分类是低效的.
- 深度学习为书封面识别和分类提供了自动化解决方案.
研究的目的:
- 开发一个优化的深度学习模型,用于准确的书封面分类.
- 为了提高图像分类任务的YOLOv8框架的性能.
- 提高书籍类型识别系统的效率和准确性.
主要方法:
- 介绍了CPPDE-YOLO模型,一种新的双卷积自适应焦点神经网络.
- 集成PConv和PWConv运营商,动态采样和高效的多尺度关注.
- 混合模型在骨干网络和DualConv框架内结合并行和点对点卷积.
主要成果:
- 与原来的YOLOv8.8相比,CPPDE-YOLO模型表现出优越的性能.
- 在真实数据集上实现了1.1%的Top_1准确度改善和1.0%的Top_5准确度改善.
- 验证了拟议的算法在增强书籍类型分类方面的有效性.
结论:
- CPPDE-YOLO模型显著提高了书封面分类的准确性.
- 集成先进的卷积运算符和注意力机制是有效的.
- 拟议的方法为自动化书籍类型分类提供了更有效,更精确的解决方案.
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