## 2 Related Works  
### Related Research on Contact Lenses  
Regarding vision inspection in the field of contact lenses, the following studies are available and are reviewed in this section.  
[PERSON] et al. [24] focused on biometric authentication technologies to enhance the security of iris recognition systems, particularly detection techniques that verify the authenticity of iris patterns. Their study aimed to detect spoofing attacks on iris recognition systems and determine whether contact lenses were worn based on iris images. [PERSON] et al. [25] concentrated on detecting cosmetic contact lenses in long-distance iris recognition systems. Their paper proposes methodologies to improve the accuracy of iris recognition technology, specifically by introducing a lightweight cosmetic contact lens detection system capable of recognizing irises from a distance.  
The studies by [24; 25] were conducted in a context different from this research. While these studies focused on enhancing the security of iris recognition systems or determining whether contact lenses are being worn, this study emphasizes the precise detection of physical defects in contact lenses. Because of these differences, their perspectives and objectives do not align with the scope and purpose of this study.  
[PERSON] et al. [26] employed CNN-based deep learning models such as ResNet101, GoogLeNetV2 [27], InceptionV4, DenseNet121 [28], and MobileNet [29]. They generated training and validation data through preprocessing and augmentation of colored contact lens images and compared the defect detection rates for each RGB and HSV channel. The accuracies of the aforementioned models were 89.74%, 84.46%, 95.43%, 82.80%, and 89.74%, respectively, with InceptionV4 achieving the highest defect detection accuracy of 95.43% in the RGB channel. Furthermore, in most models, the RGB channel demonstrated higher accuracy than the HSV channel. However, the defect detection objectives explored in [PERSON] et al. [26] differ from those of this study because of differences in the target processes. [PERSON] et al. [26] focused on specific defect types that occur during the coloring process in the manufacturing of colored contact lenses, whereas this study emphasizes the detection of physical defects that may arise during the general manufacturing process of standard contact lenses.  
[PERSON] et al. [22] focused on the development of an AI-based automatic optical inspection (AOI) system for detecting and classifying defects that may occur during the manufacturing of contact lenses. Specifically, they explored methodologies for effectively detecting defects using various CNN models, considering the complexity of the colored contact lens manufacturing process. To achieve this, they generated training and validation data through the preprocessing and augmentation of colored contact lens images.  
However, the defect detection accuracy of the AI models used in [PERSON] et al. [22] was 59.5%. Although this demonstrates a certain level of effectiveness in the implementation of AI-based AOI systems, there is room for improvement. This underscores the need for further research and model optimization to achieve the high levels of accuracy and efficiency required for manufacturing processes.  
This study aims to precisely detect and classify the physical defects that occur during the contact lens manufacturing process. Specifically, we focus on developing an advanced convolutional neural network (CNN) model based on a defect-adaptive hierarchical structure (DHS). This model processes data through pathways specialized for different defect types, thereby achieving higher accuracy and efficiency.