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Image data assessment approach for deep learning-based metal surface defect-detection systems
2021
IEEE Access
The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are timeconsuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect
doi:10.1109/access.2021.3068256
fatcat:5nhgnxuwqjhrrpsgayygcu5xb4