A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes
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 s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/18/6261 |
| _version_ | 1827681278270373888 |
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| author | Chousak Chousangsuntorn Teerawat Tongloy Santhad Chuwongin Siridech Boonsang |
| author_facet | Chousak Chousangsuntorn Teerawat Tongloy Santhad Chuwongin Siridech Boonsang |
| author_sort | Chousak Chousangsuntorn |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-10T07:13:56Z |
| format | Article |
| id | doaj.art-e30eb85589a54d9b821b2b8a1c310387 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:56Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e30eb85589a54d9b821b2b8a1c3103872023-11-22T15:14:09ZengMDPI AGSensors1424-82202021-09-012118626110.3390/s21186261A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing ProcessesChousak Chousangsuntorn0Teerawat Tongloy1Santhad Chuwongin2Siridech Boonsang3Department of Electrical Engineering, School of Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandCenter of Industrial Robot and Automation (CiRA), College of Advanced Manufacturing Innovation, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandCenter of Industrial Robot and Automation (CiRA), College of Advanced Manufacturing Innovation, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Electrical Engineering, School of Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandThis 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).https://www.mdpi.com/1424-8220/21/18/6261convolution neural networksoptical character recognitioncharacter classificationhard disk drive |
| spellingShingle | Chousak Chousangsuntorn Teerawat Tongloy Santhad Chuwongin Siridech Boonsang A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes Sensors convolution neural networks optical character recognition character classification hard disk drive |
| title | A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes |
| title_full | A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes |
| title_fullStr | A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes |
| title_full_unstemmed | A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes |
| title_short | A Deep Learning System for Recognizing and Recovering Contaminated Slider Serial Numbers in Hard Disk Manufacturing Processes |
| title_sort | deep learning system for recognizing and recovering contaminated slider serial numbers in hard disk manufacturing processes |
| topic | convolution neural networks optical character recognition character classification hard disk drive |
| url | https://www.mdpi.com/1424-8220/21/18/6261 |
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