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.
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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