Anomaly segmentation, which localizes defective areas, is an important
component in large-scale industrial manufacturing. However, most recent
researches have focused on anomaly detection. This paper proposes a novel
anomaly segmentation network (AnoSeg) that can directly generate an accurate
anomaly map using self-supervised learning. For highly accurate anomaly
segmentation, the proposed AnoSeg considers three novel techniques: Anomaly
data generation based on hard augmentation, self-supervised learning with
pixel-wise and adversarial losses, and coordinate channel concatenation. First,
to generate synthetic anomaly images and reference masks for normal data, the
proposed method uses hard augmentation to change the normal sample
distribution. Then, the proposed AnoSeg is trained in a self-supervised
learning manner from the synthetic anomaly data and normal data. Finally, the
coordinate channel, which represents the pixel location information, is
concatenated to an input of AnoSeg to consider the positional relationship of
each pixel in the image. The estimated anomaly map can also be utilized to
improve the performance of anomaly detection. Our experiments show that the
proposed method outperforms the state-of-the-art anomaly detection and anomaly
segmentation methods for the MVTec AD dataset. In addition, we compared the
proposed method with the existing methods through the intersection over union
(IoU) metric commonly used in segmentation tasks and demonstrated the
superiority of our method for anomaly segmentation.

本文提出了一种新型的异常分割网络（AnoSeg），能够使用自监督学习直接生成准确的异常图，并通过像素精度和对抗损失以及坐标通道拼接等三种新技术保证异常分割的高精度性能。我们的实验表明，该方法优于目前 MVTec AD 数据集上的现有异常检测和异常分割方法，通过 IoU 指标也证明了其在异常分割方面的优越性。