Seven Segment Display Detection and Recognition via Deep Learning Technique
| dc.contributor.author | Suttapakti, Ungsumalee | |
| dc.contributor.author | Titijaroonroj, Taravichet | |
| dc.contributor.author | Nunsong, Walairach | |
| dc.contributor.author | Kakanopas, Donyarut | |
| dc.date.accessioned | 2026-08-06T10:35:25Z | |
| dc.date.available | 2026-08-06T10:35:25Z | |
| dc.date.issued | 2022-01-01 | |
| dc.description.abstract | Seven segment display detection and recognition play an important role in determining the status of manufacturing machines. However, in some industrial factories, employees are still assigned to manually record the status of the seven segment displays. This is not real-Time tracking status and it is easy to make the typos or mistakes while collecting data. Hence, image processing and machine vision are used to automatically detect and recognize images from the seven segment displays. In this paper, the Cascade R-CNN is applied to automatically detect and recognize seven-segment displays in a single model-End-To-end learning because this method is efficient and flexible. The Cascade R-CNN method achieves precision, recall, and F1-score of 0.999 which are higher than conventional methods and the state-of-The-Art methods, including Faster R-CNN, RetinaNet, NAS-FPN, CornerNet, and CenterNet. Although the recognition accuracy of Cascade R-CNN is slightly lower than those of YOLOv3 and CornerNet, its accuracy is still higher than the Faster R-CNN, SSD, RetinaNet, NAS-FPN, and CenterNet. This method can automatically detect and recognize the digits on seven-segment display in a single model, thus improving the effectiveness for detecting and recognizing seven-segment display images. | |
| dc.identifier.citation | 19th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2022, 2022 | |
| dc.identifier.doi | 10.1109/ECTI-CON54298.2022.9795620 | |
| dc.identifier.other | 2-s2.0-85133367684 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/12799 | |
| dc.source | 19th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2022 | |
| dc.subject | Cascade R-CNN | |
| dc.subject | Convolutional neural network | |
| dc.subject | Deep learning | |
| dc.subject | Image detection and recognition | |
| dc.subject | Seven segment display | |
| dc.title | Seven Segment Display Detection and Recognition via Deep Learning Technique | |
| dc.type | Conference Paper |