A deep learning system for recognizing and recovering contaminated slider serial numbers in hard disk manufacturing processes
| dc.contributor.author | Chousangsuntorn, Chousak | |
| dc.contributor.author | Tongloy, Teerawat | |
| dc.contributor.author | Chuwongin, Santhad | |
| dc.contributor.author | Boonsang, Siridech | |
| dc.date.accessioned | 2026-08-06T10:33:26Z | |
| dc.date.available | 2026-08-06T10:33:26Z | |
| dc.date.issued | 2021-09-01 | |
| dc.description.abstract | This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms). | |
| dc.identifier.citation | Sensors, 21(18), 2021 | |
| dc.identifier.doi | 10.3390/s21186261 | |
| dc.identifier.issn | 14248220 | |
| dc.identifier.other | 2-s2.0-85115047505 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/12258 | |
| dc.source | Sensors | |
| dc.subject | Character classification | |
| dc.subject | Convolution neural networks | |
| dc.subject | Hard disk drive | |
| dc.subject | Optical character recognition | |
| dc.title | A deep learning system for recognizing and recovering contaminated slider serial numbers in hard disk manufacturing processes | |
| dc.type | Article |