Defect detection of nuclear fuel assembly based on deep neural network

Fuel assembly may be damaged during production, transportation and operation, which affects the safety of the nuclear power plant directly. Manual visual inspection is often used to identify the defect of fuel assemblies. This method is a time-consuming, tedious and subjective process. The accurate detection is highly depended on personnel experience and technician's attention. Although some traditional defect detection algorithms based on computer vision have been used, their performances with respect to fuel assembly detection are not good as expected. In this work, based on the state-of-the-art deep neural network, the Faster R-CNN is proposed to detect the scratch of nuclear fuel assembly. The proposed framework achieves 0.98 TPR against 0.1 FPR that is significantly higher than most of current appearance detection method. (C) 2019 Elsevier Ltd. All rights reserved.