Ye In Park

Jouwon Song, Kyeongbo Kong, Ye-In Park et al.

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.

Jou Won Song, Kyeongbo Kong, Ye In Park et al.

Anomaly detection is a task that recognizes whether an input sample is included in the distribution of a target normal class or an anomaly class. Conventional generative adversarial network (GAN)-based methods utilize an entire image including foreground and background as an input. However, in these methods, a useless region unrelated to the normal class (e.g., unrelated background) is learned as normal class distribution, thereby leading to false detection. To alleviate this problem, this paper proposes a novel two-stage network consisting of an attention network and an anomaly detection GAN (ADGAN). The attention network generates an attention map that can indicate the region representing the normal class distribution. To generate an accurate attention map, we propose the attention loss and the adversarial anomaly loss based on synthetic anomaly samples generated from hard augmentation. By applying the attention map to an image feature map, ADGAN learns the normal class distribution from which the useless region is removed, and it is possible to greatly reduce the problem difficulty of the anomaly detection task. Additionally, the estimated attention map can be used for anomaly segmentation because it can distinguish between normal and anomaly regions. As a result, the proposed method outperforms the state-of-the-art anomaly detection and anomaly segmentation methods for widely used datasets.