Spherical image acquisition and defect detection of nuclear fuel based on the line scan

Abstract

Graphite balls are used as matrix materials of nuclear fuel. To ensure the safe use of nuclear energy, it is necessary to detect the surface defects of graphite balls to screen out the balls that do not meet safety requirements. However, due to the small surface defects and complex background of graphite balls, the detection accuracy based on traditional detection methods is difficult to meet engineering requirements. To solve the above problems, this paper designs an image acquisition device for graphite balls based on line scanning to collect spherical images and designs an improved algorithm based on the U2-Netp network to solve the problem of low accuracy of defect detection. Firstly, the algorithm replaces the up-sampling in the RSU module with cubic linear interpolation to enhance the smoothness of the image. Secondly, spatial channel attention and multi-feature fusion modules are built between the encoder and the decoder to improve the accuracy of the model to obtain interesting information. Finally, the prediction output of Sigmoid and Threshold fusion is designed to solve the problems caused by the gradient disappearance and the uneven polarity of positive and negative samples. Tested on the actual collected data set, the improved U2-Netp algorithm improved the average accuracy of detecting surface defects on graphite balls by 8.1% compared to the original U2-Netp algorithm, reaching 85.3%.