Clamp Dot Image Classification Using Neural Network
| dc.contributor.author | Srikam, Krittapak | |
| dc.contributor.author | Saenthon, Anakkapon | |
| dc.date.accessioned | 2026-08-06T10:44:43Z | |
| dc.date.available | 2026-08-06T10:44:43Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | In this paper, we discuss the classification of images captured by a machine camera while assembling components. To crop out specific points of interest, we employ image processing. Additionally, we utilize deep learning techniques, specifically convolutional neural networks, to identify the type of equipment being assembled. This approach allows us to determine and record specific parts within a device. However, the main challenge of this project is to achieve both high accuracy and the shortest possible prediction time. | |
| dc.identifier.citation | Sensors and Materials, 36(4), 1431-1439, 2024 | |
| dc.identifier.doi | 10.18494/SAM5000 | |
| dc.identifier.issn | 09144935 | |
| dc.identifier.other | 2-s2.0-85191559610 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15278 | |
| dc.source | Sensors and Materials | |
| dc.subject | CNN | |
| dc.subject | image processing | |
| dc.subject | neural network | |
| dc.subject | OpenCV | |
| dc.subject | TensorFlow | |
| dc.title | Clamp Dot Image Classification Using Neural Network | |
| dc.type | Article |