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用深度学习识别手工织面料.
Lipi B Mahanta1, Deva Raj Mahanta2, Taibur Rahman2
1Mathematical and Computational Sciences Division, Institute of Advanced Study in Science & Technology (IASST) (An Autonomous R&D Institute Under Department of Science & Technology), Vigyan Path, Paschim Boragaon, P.O. Garchuk, Guwahati, Assam, 781035, India. lbmahanta@iasst.gov.in.
Scientific reports
|April 4, 2024
概括
这项研究开发了一种人工智能工具,可以从假冒中识别真实的印度手工织物"gamucha"毛巾. 一个新的深度学习模型的性能优于现有的模型,为保护织遗产提供了高效和准确的检测.
科学领域:
- 织科学 织科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 印度的手工织物行业对于文化遗产和工匠的生计至关重要.
- 假冒的强力织布产品威胁到真正的手工织布产品的真实性和市场.
- 区分出真正的手工织织品,比如
研究的目的:
- 开发一种人工智能驱动的工具,用于自动检测真实的手工织物产品.
- 为了区分真正的手工织品.
主要方法:
- 在17484个手工织物和电力织物毛巾图像上训练了六个先前存在的深度学习架构 (VGG16,VGG19,ResNet50,InceptionV3,InceptionResNetV2,DenseNet201).
- 为同一个图像数据集开发和训练了一种新的深度学习模型.
- 基于验证准确性,损失和计算效率来评估模型性能.
主要成果:
- 这种新的深度学习模型表现出比预先训练的模型更高的性能.
- 拟议的模型实现了更高的验证准确性和更低的验证损失.
- 预先训练的模型在对未见的数据进行概括时遇到了困难,并提出了计算挑战.
结论:
- 一个新的AI模型为验证手工织物产品提供了高效和准确的解决方案.
- 开发的方法表明了在织品认证中的可扩展性和更广泛应用的潜力.
- 这种计算机辅助的方法是保护手工织物遗产免受模仿的突破性方法.
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