A Self-Supervised CNN for Particle Inspection on Optical Element

Abstract

In high-power laser instruments, optical elements play a significant role. Particles on the optical element degrade the system performance and even cause damage to the optical element. In this article, a particle inspection model based on self-supervised convolutional neural networks (CNNs) and transfer learning is proposed. The self-supervised network that is built on a rotation-flip-invariant pretext task is used to transform the image from grayscale feature to rotation-flip-invariant feature. Then, the learned feature is transferred to the central-pixel classification network that is fine-tuned on a small labeled dataset. The experiments show that the classification accuracy of our proposed method is 97.90%, which is higher than the other compared methods. For the whole image prediction, through feature reuse and pointwise convolution, the central-pixel classification network is adapted to the particle inspection network efficiently with minor changes. Since the method utilizes massive unlabeled data and is fine-tuned on a small number of labeled samples, it has the potential to be used in industrial production.