DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses
Vision-based inspection systems are essential for quality control in manufacturing industries, and advances in artificial intelligence (AI) have significantly enhanced their accuracy. However, the high-precision requirements of products such as contact lenses demand even more robust inspection metho...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/5/2697 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850053229507772416 |
|---|---|
| author | Sung-Hoon Kim Seong-Jong Joo Kwan-Hee Yoo |
| author_facet | Sung-Hoon Kim Seong-Jong Joo Kwan-Hee Yoo |
| author_sort | Sung-Hoon Kim |
| collection | DOAJ |
| description | Vision-based inspection systems are essential for quality control in manufacturing industries, and advances in artificial intelligence (AI) have significantly enhanced their accuracy. However, the high-precision requirements of products such as contact lenses demand even more robust inspection methods. This paper introduces a novel defect-adaptive hierarchical structure convolution neural network (DHS-CNN) model based on InceptionV4. The proposed model architecture reflects the manufacturing process and defect types, and we developed a custom loss function to suit this multi-output hierarchical design. Experimental results on a dataset of 2800 contact lens images revealed that the proposed model improved accuracy by 2.08% over the baseline model. These findings suggest that the defect-adaptive hierarchical structure and customized loss function offer substantial improvements in the vision-based inspection of contact lenses and may enhance AI-driven quality control processes in other manufacturing sectors. |
| format | Article |
| id | doaj-art-c948d77bff38401db16a8205996bb2a2 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c948d77bff38401db16a8205996bb2a22025-08-20T02:52:35ZengMDPI AGApplied Sciences2076-34172025-03-01155269710.3390/app15052697DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact LensesSung-Hoon Kim0Seong-Jong Joo1Kwan-Hee Yoo2Department of Computer Science, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju 28644, Republic of KoreaDepartment of Computer Science, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju 28644, Republic of KoreaDepartment of Computer Science, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju 28644, Republic of KoreaVision-based inspection systems are essential for quality control in manufacturing industries, and advances in artificial intelligence (AI) have significantly enhanced their accuracy. However, the high-precision requirements of products such as contact lenses demand even more robust inspection methods. This paper introduces a novel defect-adaptive hierarchical structure convolution neural network (DHS-CNN) model based on InceptionV4. The proposed model architecture reflects the manufacturing process and defect types, and we developed a custom loss function to suit this multi-output hierarchical design. Experimental results on a dataset of 2800 contact lens images revealed that the proposed model improved accuracy by 2.08% over the baseline model. These findings suggest that the defect-adaptive hierarchical structure and customized loss function offer substantial improvements in the vision-based inspection of contact lenses and may enhance AI-driven quality control processes in other manufacturing sectors.https://www.mdpi.com/2076-3417/15/5/2697vision inspection in manufacturingautomated optical inspectiondeep learningartificial intelligencedefect-adapted hierarchical structuremulti-output |
| spellingShingle | Sung-Hoon Kim Seong-Jong Joo Kwan-Hee Yoo DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses Applied Sciences vision inspection in manufacturing automated optical inspection deep learning artificial intelligence defect-adapted hierarchical structure multi-output |
| title | DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses |
| title_full | DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses |
| title_fullStr | DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses |
| title_full_unstemmed | DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses |
| title_short | DHS-CNN: A Defect-Adaptive Hierarchical Structure CNN Model for Detecting Anomalies in Contact Lenses |
| title_sort | dhs cnn a defect adaptive hierarchical structure cnn model for detecting anomalies in contact lenses |
| topic | vision inspection in manufacturing automated optical inspection deep learning artificial intelligence defect-adapted hierarchical structure multi-output |
| url | https://www.mdpi.com/2076-3417/15/5/2697 |
| work_keys_str_mv | AT sunghoonkim dhscnnadefectadaptivehierarchicalstructurecnnmodelfordetectinganomaliesincontactlenses AT seongjongjoo dhscnnadefectadaptivehierarchicalstructurecnnmodelfordetectinganomaliesincontactlenses AT kwanheeyoo dhscnnadefectadaptivehierarchicalstructurecnnmodelfordetectinganomaliesincontactlenses |