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Damage to shielding sheets on X-ray protective clothing may be a cause of increased radiation exposure. To prevent increased radiation exposure, periodic quality control of shielding sheets is needed. For quality management, a record of the size of damage is required after checking for the existence of damage, and this requires a great deal of effort and time. Additionally, the detection model created from the images of the shielding sheets, limited by the number of samples, is predicted to have a low detection precision. The purpose of this study was to automate damage area detection and area measurement using artificial damage images and a damage detection model created using deep learning.

Automation of Damage Detection and Damage Area Measurement of X-ray Protective Clothing Using Deep Learning