Surface Defect Detection Using Deep U-Net Network Architectures
| dc.authorid | UZEN, Huseyin/0000-0002-0998-2130 | |
| dc.authorid | Hanbay, Davut/0000-0003-2271-7865 | |
| dc.authorwosid | UZEN, Huseyin/CZK-0841-2022 | |
| dc.authorwosid | Hanbay, Davut/AAG-8511-2019 | |
| dc.contributor.author | Uzen, Huseyin | |
| dc.contributor.author | Turkoglu, Muammer | |
| dc.contributor.author | Hanbay, Davut | |
| dc.date.accessioned | 2024-08-04T21:01:15Z | |
| dc.date.available | 2024-08-04T21:01:15Z | |
| dc.date.issued | 2021 | |
| dc.department | İnönü Üniversitesi | en_US |
| dc.description | 29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORK | en_US |
| dc.description.abstract | Surface defects detection in products used in industry such as steel, fabric and marble is very important in terms of increasing product quality and preventing financial losses. However, automatic surface defects detection is a very difficult problem due to the complexity and diversity of surface defects. In this study, U-net based VGG16-Unet and Resnet34-Unet network models are proposed for Surface defects detection. The proposed model used spatial features in the first layers together with deep semantic features. In the proposed network models, the trained weights of the VGG16 and Resnet34 network architectures were used for the input parameters of the Unet architecture. In experimental studies, the highest F1-score value for MT and AITEX data sets was obtained as 91.07% and 94.67%, respectively, with the proposed Resnet34-Unet model. According to the results, it was observed that the defective areas showing similarity with the background were successfully separated by using the proposed model. | en_US |
| dc.description.sponsorship | IEEE,IEEE Turkey Sect | en_US |
| dc.identifier.doi | 10.1109/SIU53274.2021.9477790 | |
| dc.identifier.isbn | 978-1-6654-3649-6 | |
| dc.identifier.uri | https://doi.org/10.1109/SIU53274.2021.9477790 | |
| dc.identifier.uri | https://hdl.handle.net/11616/104217 | |
| dc.identifier.wos | WOS:000808100700034 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 29th Ieee Conference on Signal Processing and Communications Applications (Siu 2021) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Surface Defects Detection | en_US |
| dc.subject | Feature Extraction | en_US |
| dc.subject | Unet | en_US |
| dc.subject | Deep Learning | en_US |
| dc.title | Surface Defect Detection Using Deep U-Net Network Architectures | en_US |
| dc.type | Conference Object | en_US |
