Yang Guoliang, Zhou Gaohao, Wang Changyuan, Mu Jing, Yang Zhenhu, Li Yuan, Su Junhong
School of Optoelectronic Engineering, Xi'an Technological University, China.
School of Computer Science and Software Engineering, Xi'an Technological University, China.
Heliyon. 2023 Feb 11;9(2):e13701. doi: 10.1016/j.heliyon.2023.e13701. eCollection 2023 Feb.
Optical coating damage detection is a part of both industrial production and scientific research. Traditional methods require sophisticated expert systems or experienced front-line producers, and the cost of these methods rises dramatically when film types or inspection environments change. In practice, it has been found that customized expert systems imply a significant investment of time and money, and we expect to find a method that can perform this task automatically and quickly, while at the same time the method should be adaptable to the later addition of coating types and the ability to identify damage kinds. In this paper, we propose a deep neural network-based detection tool that splits the task into two parts: damage classification and damage degree regression. Introduces attention mechanisms and Embedding operations to enhance the performance of the model. It was found that the damage type detection accuracy of our model reached 93.65%, and the regression loss was kept within 10% on different data sets. We believe that deep neural networks have great potential to tackle industrial defect detection by significantly reducing the design cost and time of traditional expert systems, while gaining the ability to detect entirely new damage types at a fraction of the cost.
光学涂层损伤检测是工业生产和科学研究的一部分。传统方法需要复杂的专家系统或经验丰富的一线生产者，当薄膜类型或检测环境发生变化时，这些方法的成本会急剧上升。在实践中发现，定制专家系统意味着大量的时间和资金投入，我们期望找到一种能够自动快速执行此任务的方法，同时该方法应适应后期添加涂层类型以及识别损伤种类的能力。在本文中，我们提出了一种基于深度神经网络的检测工具，该工具将任务分为两部分：损伤分类和损伤程度回归。引入注意力机制和嵌入操作以提高模型性能。结果发现，我们模型的损伤类型检测准确率达到93.65%，在不同数据集上回归损失保持在10%以内。我们相信，深度神经网络在解决工业缺陷检测方面具有巨大潜力，通过显著降低传统专家系统的设计成本和时间，同时以极低的成本获得检测全新损伤类型的能力。
