Deep learning application in detecting glass defects with color space conversion and adaptive histogram equalization
| dc.contributor.author | Sarı, Filiz | |
| dc.contributor.author | Ulaş, Ali Burak | |
| dc.date.accessioned | 2022-06-23T05:58:21Z | |
| dc.date.available | 2022-06-23T05:58:21Z | |
| dc.date.issued | 2022 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | Manually detecting defects on the surfaces of glass products is a slow and time-consuming process in the quality control process, so computer-aided systems, including image processing and machine learning techniques are used to overcome this problem. In this study, scratch and bubble defects of the jar, photographed in the studio with a white matte background and a-60 degrees peak angle, are investigated with the Yolo-V3 deep learning technique. Obtained performance is 94.65% for the raw data. Color space conversion (CSC) techniques, HSV and CIE-Lab Luv, are applied to the resulting images. V channels select for preprocessing. While the HSV method decreases the performance, an increase has been observed in the CIE-Lab Luv method. With the CIE-Lab Luv method, to which is applied the adaptive histogram equalization, the maximum recall, precision, and F1-score reach above 97%. Also, Yolo-V3 compared with the Faster R-CNN, it is observed that Yolo-V3 gave better results in all analyzes, and the highest overall accuracy is achieved in both methods when adaptive histogram equalization is applied to CIE-Lab Luv. | |
| dc.identifier.doi | 10.18280/ts.390238 | |
| dc.identifier.endpage | 736 | en_US |
| dc.identifier.issn | 0765-0019 | |
| dc.identifier.issn | 1958-5608 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 731 | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.18280/ts.390238 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/9468 | |
| dc.identifier.volume | 39 | en_US |
| dc.identifier.wos | WOS:000798489300038 | |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | International Information and Engineering Technology | |
| dc.relation.ispartof | Traitement du Signal | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Adaptive Histogram Equalization | |
| dc.subject | Color Space Conversion | |
| dc.subject | Glass Defect Detection | |
| dc.subject | Deep Learning | |
| dc.title | Deep learning application in detecting glass defects with color space conversion and adaptive histogram equalization | |
| dc.type | Article |
