Earticle

초록

영어

For the anomaly detection task, previously presented deep learning approaches suffer from one potential issue in the testing stage, the resultant output image has noise and missing anomaly area. To deal with this issue, we present a novel two-stage convolutional neural network (CNN) for anomaly detection. In the training stage, the first model is trained by inserting pseudo-anomalies, while the second model is trained by a superpixel technique which segments the image refined by the first model. The superpixel technique can recover partially visible anomaly patterns and suppress noise outside the recovered anomaly patches. We trained the proposed model using an industrial dataset MVTec and compared its performance with state-of-the-art pseudo-anomalous method [11]. Our method shows comparable pixel based percentage area under the receiver operating characteristic (%AUROC) of 96.0% which is only 1.3% less than the performance of DRAEM. However, our model uses four times less number of parameters.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Proposed Model
B. Proposed Approach
C. Training Methodology
III. CONCLUSION
REFERENCES

키워드

저자

• Shah Rizwan Ali [ College of Electrical and Computer Engineering Chungbuk National University, Cheongju, South Korea ]
• Kim HyungWon [ College of Electrical and Computer Engineering Chungbuk National University, Cheongju, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004