通过深度学习增强艺术分析:基于SSD和FPT的图形艺术元素识别模型
1Shandong College of Arts, Jinan, China.
PeerJ. Computer science
|January 10, 2024
概括
本研究引入了一种增强的深度学习模型,用于识别图形艺术元素,提高艺术家从艺术品中学习的能力. 这种精细的方法显示了与原来的单射 MultiBox 检测器 (SSD) 算法相比显著的性能提升.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 艺术分析 艺术分析
背景情况:
- 准确识别图形艺术中的元素对于艺术家的学习和欣赏至关重要.
- 深度学习方法为精确分析复杂的视觉设计提供了潜力.
- 现有的物体检测模型可能需要改进,以进行细微的艺术元素识别.
研究的目的:
- 开发和评估一种增强的深度学习模型,用于精确识别图形艺术设计中的各种元素.
- 为了提高艺术作品中对象检测的准确性和稳定性.
- 通过先进的计算分析,帮助艺术家欣赏和学习艺术品.
主要方法:
- 将注意力机制集成到一个增强的单拍多盒探测器 (SSD) 模型中.
- 改进了SSD模型的功能融合结构,并提供了远程注意力信息.
- 针对物体检测的特征金字塔变压器 (FPT) 注意力机制的改进.
主要成果:
- 这种精细的方法在四个评估指标上表现出与原始SSD算法相比的优异性能.
- 在关键绩效指标中观察到1.52%,1.89%,3.09%和2.57%的改善.
- 定性测试证实了该方法的准确性,稳定性和通用性,特别是在复杂的艺术作品中.
结论:
- 提议的注意力增强的SSD模型与精致的FPT注意力显著改善了艺术设计元素的识别.
- 该方法为艺术分析提供了有价值的工具,增强了艺术家的理解和学习.
- 该方法显示了在多样化和具有挑战性的艺术分析场景中应用的强大潜力.
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