Deep learning-based defect segmentation is one of the important tasks of machine vision in automated inspection. Supervised learning methods have recently achieved remarkable performance on this task. However, the effectiveness of the supervised methods is limited by the scarcity and high cost of pixel-level annotation of training data. Semi-supervised learning methods have been proposed for training deep learning networks using a limited amount of labeled data along with additional unlabeled data for image segmentation. Most of these methods are based on consistency regularization and pseudo labeling, where the predictions on unlabeled samples often come with noise and are unreliable, resulting in poor segmentation performance. To alleviate this problem, we propose uncertainty-aware pseudo labels, which are generated from dynamically mixed predictions of multiple decoders that leverage a shared encoder network. The estimated uncertainty guides the pseudo-label-based supervision and regularizes the training when using the unlabeled samples. In our experiments on four public datasets for defect segmentation, the proposed method outperformed the fully supervised baseline and six state-of-the-art semi-supervised segmentation methods. We also conducted an extensive ablation study to demonstrate the effectiveness of our approach in various settings. The implementation code for this work is available at https://github.com/djene-mengistu/UAPS.

基于深度学习的缺陷分割是机器视觉在自动化检测中的重要任务之一。监督学习方法最近在这项任务上取得了显着的成绩。然而，监督方法的有效性受到训练数据像素级注释的稀缺性和高成本的限制。已经提出了半监督学习方法，用于使用有限数量的标记数据以及用于图像分割的附加未标记数据来训练深度学习网络。这些方法大多数基于一致性正则化和伪标记，其中对未标记样本的预测常常带有噪声且不可靠，导致分割性能较差。为了缓解这个问题，我们提出了不确定性感知伪标签，它们是通过利用共享编码器网络的多个解码器的动态混合预测生成的。估计的不确定性指导基于伪标签的监督，并在使用未标记样本时规范训练。在我们对四个公共数据集进行缺陷分割的实验中，所提出的方法优于完全监督的基线和六种最先进的半监督分割方法。我们还进行了广泛的消融研究，以证明我们的方法在各种情况下的有效性。这项工作的实现代码可在 在我们对四个公共数据集进行缺陷分割的实验中，所提出的方法优于完全监督的基线和六种最先进的半监督分割方法。我们还进行了广泛的消融研究，以证明我们的方法在各种情况下的有效性。这项工作的实现代码可在 在我们对四个公共数据集进行缺陷分割的实验中，所提出的方法优于完全监督的基线和六种最先进的半监督分割方法。我们还进行了广泛的消融研究，以证明我们的方法在各种情况下的有效性。这项工作的实现代码可在https://github.com/djene-mengistu/UAPS。