## 3 Defect-Adapted Hierarchical Deep Learning Model
### Proposed Defect-Adapted Hierarchical Deep Learning Model  
In the initial stage of the modified model shown in Figure 11b, the Stem and Inception A layers were used to determine the presence or absence of a lens shape. The Stem layer, positioned at the beginning of the model, focuses on extracting high-level abstract features from the input image. This layer consists of multiple convolution, pooling, and normalization layers. The convolution layers use filters (kernels) to identify basic shapes and patterns in the image, whereas the pooling layers reduce the image size, retain essential information, and improve computational efficiency. The normalization layers help stabilize and accelerate the learning process, thereby ensuring robust feature extraction. Normalization layers help prevent the data from being overly skewed, thereby improving the stability and performance of the model. The Inception A layer, which is a variant of the inception module, is designed to capture image features at multiple scales by arranging convolution layers with kernels of different sizes and pooling layers in parallel. This allows the model to simultaneously recognize patterns of various sizes within the same image region, thereby enabling the extraction of richer and more diverse features. By leveraging these diverse kernels, Inception A excels in identifying both large and small objects or patterns within an image. These layers play a crucial role in identifying the basic shape of the lens and in detecting significant defects. Based on this role, the **no_lens** and **etc_abnormal** classes were designed to be determined at this stage of the model. Next, the Reduction A and Inception B layers were employed to determine whether the circular edge was maintained. The Reduction A layer reduces the dimensionality of the feature maps in the neural network while preserving critical features. This layer enhances the computational efficiency of the network and prevents overfitting. Reduction A executes  
Figure 10: Consider defect type and size when designing a defect-adapted hierarchical structure. various convolution and pooling operations in parallel, effectively reducing the dimensions of the input data while retaining essential information. The Inception B layer features a more complex and deeper convolutional structure designed to process finer features such as circular edges. This layer uses kernels of different sizes in parallel to capture features at multiple scales simultaneously, allowing for a more detailed and comprehensive analysis of intricate patterns. The Inception B layer covers larger regions of an image without missing intricate patterns and is particularly adept at detecting and analyzing complex shapes and changes, making it highly valuable for detailed image analysis. These layers focus on precisely analyzing the shape and damage to the lens edges. Consequently, this stage was specifically designed to classify the **burr** and **broken** classes. In the final stage, the Reduction B and Inception C layers were employed to identify linear and donut-shaped defects. The Inception C layer, located in the deeper part of the network, is equipped with a complex structure capable of simultaneously processing image features at multiple scales. This layer uses both small and large kernels in parallel to capture fine details in the image. Inception C enables precise image analysis and enhances classification and recognition accuracy. These layers are designed to handle complex and fine defect types with precision. Specifically, the final layer continues the feature extraction process for previously classified classes, reflecting the characteristics of the defects in its design. To evaluate how the proposed adaptive hierarchical structure improves accuracy compared to the existing InceptionV4 and EfficientNet models in relation to the goals of this study, we adopted a method that retains the core layers of the InceptionV4 model while incorporating the DHS.