SAIT: Harnessing Sparse Annotations and Intrinsic Tasks for Semisupervised Aeroengine Defect Segmentation

In aeroengine maintenance, endoscopic imaging serves as a crucial tool for detecting blade defects and evolves toward intelligence driven by computer vision technology. Currently, supervised-learning-based defect segmentation methods mainly rely on extensive pixel-level annotations, making it laborious and time consuming. This article shifts focus to the abundant unlabeled data in real-world scenarios and introduces an innovative semisupervised defect segmentation method termed SAIT. Within this framework, three parallel self-supervised mechanisms are adeptly integrated with a semisupervised framework, aiming to bolster defect semantic segmentation with limited labeled samples. In the initial phase, by leveraging the capability of the vision transformer to dissect images into patches, four stochastic distortions are seamlessly infused into the patch sequence. Subsequently, three self-supervised tasks from image level to pixel level are achieved through a customized joint objective function paired with a tailored backbone network. In the second phase, SAIT undergoes pixel-level fine-tuning via the proposed class-centric loss, mitigating class imbalances in limited sample sizes and enhancing initial training. Experiments on a proprietary dataset demonstrate that SAIT achieved 78.42% and 86.70% in mean intersection over union and mean pixel accuracy metrics, respectively, with 25% labeled data, significantly improving the performance of existing semisupervised defect segmentation techniques. Meanwhile, experiments on the open-source dataset further indicate that SAIT holds promise for application in other industrial sectors beyond aeroengine inspection.