LAUSR

We propose a three-dimensional (3D) image inpainting system using the 3D encoder–decoder generative adversarial network (IISU3EDGAN) for providing accurate detection results in vision-based rotor dynamic balancing processes. The proposed IISU3EDGAN system integrates 3D sensors with a deep learning network to reconstruct corrupted rotor images, thereby optimizing the detection parameters. In rotor component detection processes, overexposed images caused by reflections of the metallic rotor shaft affect the accuracy and performance of vision-based inspection systems. Traditional image restoration technologies or inpainting methods are inadequate for solving this problem. By contrast, our proposed system can repair 3D overexposed images of rotors. Compared with traditional image processing methods, the proposed system can adequately manage the complexity of corrupted images. In addition, it can be used to process complex overexposed rotor images and maintain image details. The proposed system can be applied to a wider range of rotor types, and it can be used to optimize the parameters of vision-based rotor detection systems to improve the accuracy of rotor component detection. We conducted experiments and observed that by using 3D sensors and deep learning, the proposed system improved the success rate in the first round of rotor dynamic balancing, reduced the number of rounds required for balancing, and increased the rotor production output. These results thus indicate that the IISU3EDGAN system is applicable and robust and that it can be used to improve the overall efficiency of dynamic balancing on rotor production lines.