Electrical and Engineering, King Mongkuts University of Technology Thonburi, Thung Khru, Bangkok 10140, Thailand.
Tesla Inc., Austin, TX 78725, USA.
Sensors (Basel). 2022 Apr 11;22(8):2915. doi: 10.3390/s22082915.
In general, one may have access to a handful of labeled normal and defect datasets. Most unlabeled datasets contain normal samples because the defect samples occurred rarely. Thus, the majority of approaches for anomaly detection are formed as unsupervised problems. Most of the previous methods have typically chosen an autoencoder to extract the common characteristics of the unlabeled dataset, assumed as normal characteristics, and determine the unsuccessfully reconstructed area as the defect area in an image. However, we could waste the ground truth data if we leave them unused. In addition, a suitable choice of threshold value is needed for anomaly segmentation. In our study, we propose a semi-supervised setting to make use of both unlabeled and labeled samples and the network is trained to segment out defect regions automatically. We first train an autoencoder network to reconstruct defect-free images from an unlabeled dataset, mostly containing normal samples. Then, a difference map between the input and the reconstructed image is calculated and feeds along with the corresponding input image into the subsequent segmentation module. We share the ground truth for both kinds of input and train the network with binary cross-entropy loss. Additional difference images can also increase stability during training. Finally, we show extensive experimental results to prove that, with help from a handful of ground-truth segmentation maps, the result is improved overall by 3.83%.
一般来说，人们可能只能访问少量标记的正常和缺陷数据集。大多数未标记的数据集包含正常样本，因为缺陷样本很少发生。因此，大多数异常检测方法都是作为无监督问题形成的。大多数先前的方法通常选择自动编码器来提取未标记数据集的共同特征，假设为正常特征，并确定图像中重建不成功的区域为缺陷区域。然而，如果我们不使用它们，就会浪费地面实况数据。此外，还需要选择合适的异常分割阈值。在我们的研究中，我们提出了一种半监督设置，以充分利用未标记和标记的样本，并且网络被训练来自动分割出缺陷区域。我们首先训练一个自动编码器网络，从一个未标记的数据集重建无缺陷的图像，这个数据集主要包含正常样本。然后，计算输入和重建图像之间的差值图，并将其与相应的输入图像一起输入到后续的分割模块中。我们共享这两种输入的真实情况，并使用二进制交叉熵损失来训练网络。额外的差值图像也可以在训练过程中提高稳定性。最后，我们展示了广泛的实验结果，证明在少量地面实况分割图的帮助下，整体结果提高了 3.83%。
