## 5 Conclusions  
The InceptionV4 model with DHS exhibited an improvement of approximately 1.39% in accuracy on the test data compared with the original InceptionV4 model. Furthermore, when the customized loss function was applied, the accuracy on the validation data improved by 2.08% over that of the original model. These findings indicate that both DHS and the customized loss function play crucial roles in boosting the model's performance, particularly for accurately identifying and classifying complex defect patterns.  
By applying DHS and a customized loss function, the model learns more complex and finer patterns, reducing overfitting and improving generalization on the validation data. Therefore, the slightly lower accuracy observed during the training process indicates more reliable predictions, translating to enhanced performance on the validation data.  
This defect-adapted approach, which is not only applicable to contact lenses but also spans various manufacturing processes (such as defect detection based on printed circuit boards, capsule defect detection in pharmaceutical manufacturing, and packaging defect detection in food and beverages), can be utilized across many industries. These manufacturing sectors, which employ stringent management standards, essentially require high levels of accuracy. For this reason, a simple image-processing technique is not sufficient; instead, a defect-adapted approach that can accurately reflect the characteristics of the defects proves to be effective.  
In future work, we plan to conduct an analysis of the YOLOv8 [36,37] and YOLOv11 [38] libraries. These models are based on the EfficientNet model but incorporate unique YOLO techniques that enhance performance. Currently, we cannot directly use these original models because our data are in a multi-output format, which is not directly compatible with them. By analyzing these libraries, we aim to explore the potential for integrating defect-adaptive structures within the YOLO framework. This addition will enable us to extend our adaptive hierarchical structure to more sophisticated classification models, thereby improving our ability to classify defects in complex manufacturing environments. Additionally, we plan to investigate a dynamic defect-adaptive hierarchical structure--specifically, an automated system. This aligns with core concepts of Tree-CNN, which can recognize new patterns or changes in the dataset and dynamically adjust model architecture. This approach enhances flexibility and adaptability, enabling the model to respond effectively to evolving conditions. Additionally, future studies will incorporate a broader range of abnormal types, thereby expanding the current model's ability to detect and classify a wider variety of defect patterns, which is crucial for complex manufacturing environments.  
Conceptualization, S.-H.K.; Methodology, S.-H.K.; software, S.-H.K.; validation and formal analysis, S.-J.J.; statistical analysis, S.-H.K. and S.-J.J.; resources, S.-H.K. and S.-J.J.; data curation, S.-H.K.; writing--review and editing, S.-H.K.; software upgrade and data generation, S.-H.K.; project administration, K.-H.Y.; funding acquisition, K.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2025-RS-2020-II201462).  
Not applicable.  
**Data Availability Statement:** Access to the data is restricted due to proprietary constraints enforced by the data-holding enterprise. Therefore, it is not available for use upon request.  
The authors declare no conflicts of interest.