Three-stage deep learning system for recognizing contaminated serial numbers in hard disk drive: A comparison study with two-stage deep learning model

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dc.contributor.authorChousangsuntorn, Chousak
dc.contributor.authorTongloy, Teerawat
dc.contributor.authorChuwongin, Santhad
dc.contributor.authorBoonsang, Siridech
dc.date.accessioned2026-08-06T10:35:30Z
dc.date.available2026-08-06T10:35:30Z
dc.date.issued2022-01-01
dc.description.abstractThe previous two-stage deep learning model for detecting and classifying misidentified serial numbers on the defect hard disk drive (HDD) slider was proposed by authors. We found that the threshold level adjusted during preprocessing process could limit the robustness of the two-stage model in large-scale manufacturing. Thus, we proposed a three-stage deep learning model comprised of 1) region of interest (ROI) detection and cropping, 2) character detection and cropping, and 3) character classification. Object detection algorithm and classification network used in this model are based on YOLO v.4 and EfficientNet-B0. The 1000 images captured by the digital camera were used for training (600 images) and validation (400 images) of the ROI detection model. The other 1000 captured images were used for testing the performance of the proposed three-stage model, then we compared them with those obtained from the previous two-stage model. The proposed three-stage model reaches F1 score = 0.997 and recovery rate up to 95.9%, while the two-stage model yields only 0.948 and 73%, respectively.
dc.identifier.citationProceedings of SPIE the International Society for Optical Engineering, 12171, 2022
dc.identifier.doi10.1117/12.2631377
dc.identifier.issn0277786X
dc.identifier.other2-s2.0-85132966656
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12822
dc.sourceProceedings of SPIE the International Society for Optical Engineering
dc.subjectClassification
dc.subjectObject detection
dc.subjectOCR
dc.subjectYOLO
dc.titleThree-stage deep learning system for recognizing contaminated serial numbers in hard disk drive: A comparison study with two-stage deep learning model
dc.typeConference Paper

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