Khalid Salman, Azad Muhammad Muzammil, Kim Heung Soo, Yoon Yanggi, Lee Hanhyoung, Choi Kwang-Soon, Yang Yoonmo
Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea.
Korea Testing Certification, 22 Heungandaero-27-gil, Gunpo 15809, Gyeonggi-do, Republic of Korea.
Gels. 2024 Aug 6;10(8):517. doi: 10.3390/gels10080517.
Oil paintings represent significant cultural heritage, as they embody human creativity and historical narratives. The preservation of these invaluable artifacts requires effective maintenance practices to ensure their longevity and integrity. Despite their inherent durability, oil paintings are susceptible to mechanical damage and chemical deterioration, necessitating rigorous conservation efforts. Traditional preservation techniques that have been developed over centuries involve surface treatment, structural stabilization, and gel-based cleaning to maintain both the integrity and aesthetic appeal of these artworks. Recent advances in artificial intelligence (AI)-powered predictive maintenance techniques offer innovative solutions to predict and prevent deterioration. By integrating image analysis and environmental monitoring, AI-based models provide valuable insights into painting preservation. This review comprehensively analyzes traditional and AI-based techniques for oil painting maintenance, highlighting the importance of adopting innovative approaches. By integrating traditional expertise with AI technology, conservators can enhance their capacity to maintain and preserve these cultural treasures for future generations.
油画代表着重要的文化遗产,因为它们体现了人类的创造力和历史叙事。保护这些珍贵的文物需要有效的维护措施,以确保其长久保存和完整性。尽管油画本身具有耐久性,但它们仍易受到机械损伤和化学变质的影响,因此需要进行严格的保护工作。几个世纪以来发展起来的传统保护技术包括表面处理、结构稳定和基于凝胶的清洁,以保持这些艺术品的完整性和美学吸引力。人工智能驱动的预测性维护技术的最新进展提供了预测和防止变质的创新解决方案。通过整合图像分析和环境监测,基于人工智能的模型为油画保护提供了有价值的见解。本综述全面分析了传统的和基于人工智能的油画维护技术,强调了采用创新方法的重要性。通过将传统专业知识与人工智能技术相结合,文物保护者可以提高其维护和保护这些文化瑰宝以供后代欣赏的能力。