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Author: search.filters.author.Chousangsuntorn, ChousakSubject: search.filters.subject.Object detection

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    Classification model of optical character recognition failures in unrecovered slider serial numbers in hard disk drive manufacturing and image capture processes
    (2022-01-01)
    Chousangsuntorn, Chousak
    ;
    Tongloy, Teerawat
    ;
    Chuwongin, Santhad
    ;
    Boonsang, Siridech
    In hard disk drive (HDD) manufacturing processes, there are unrecovered serial number images about 0.01% from the standard optical character recognition (OCR) reading and deep learning approach. We found several failures from two main causes, i.e. manufacturing process and image capture process during standard OCR reading. We proposed classification model used for recognizing the serial number reading failures based on object detection You-Only-Look- Once (YOLO) algorithm and EfficientNet-B0 classification network as well as histogram analysis. The 1000 images captured by digital camera were used for training (600 images) and validation (400 images) the ROI detection model. The other 2100 captured images were used for training and testing classification OCR failure from manufacturing process model. The model testing was performed in 900 images contained 9 causes (classes) of failures. The proposed model reaches F1 score = 0.94.
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    Three-stage deep learning system for recognizing contaminated serial numbers in hard disk drive: A comparison study with two-stage deep learning model
    (2022-01-01)
    Chousangsuntorn, Chousak
    ;
    Tongloy, Teerawat
    ;
    Chuwongin, Santhad
    ;
    Boonsang, Siridech
    The 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.