다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.47 no.1, 2024년, pp.9 - 19
김한비 (다겸(주)) , 서대호 (다겸(주))
AI-Helper
Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an i...
Akcay, S., Ameln, D., Vaidya, A., Lakshmanan, B.,？Ahuja, N., and Genc, U., Anomalib: A deep learning？library for anomaly detection, International Conference？on Image Processing, 2022, pp. 1706-1710.
Batzner, Kilian, Lars Heckler, and Rebecca Konig,？Efficientad: Accurate visual anomaly detection at？millisecond-level latencies, ArXiv: 2303.14535, 2023.
Bergmann, P., Fauser, M., Sattlegger, D., and Steger,？C., MVTec AD-A comprehensive real-world dataset for？unsupervised anomaly detection, Proceedings of the？IEEE/CVF Conference on Computer Vision and Pattern？Recognition, 2019, pp. 9592-9600.
COHEN, Niv; HOSHEN, and Yedid, Sub-image？anomaly detection with deep pyramid correspondences, ArXiv: 2005.02357, 2020.
Cui, Y., Liu, Z., and Lian, S., A Survey on Unsupervised？Anomaly Detection Algorithms for Industrial Images,？IEEE Access, 2023.
Defard, T., Setkov, A., Loesch, A., and Audigier, R.,？Padim: A Patch Distribution Modeling Framework for？Anomaly Detection and Localization, International？Conference on Pattern Recognition, 2021, pp. 475-489.
Deng, H. and Li, X., Anomaly detection via reverse？distillation from one-class embedding, Proceedings of？the IEEE/CVF Conference on Computer Vision and？Pattern Recognition, 2021, pp. 9737-9746.
Ehret, T., Davy, A., Morel, J. M., and Delbracio, M.,？Image anomalies: A review and synthesis of detection？methods, Journal of Mathematical Imaging and Vision, 2019, Vol. 61, pp. 710-743.
Gudovskiy, D., Ishizaka, S., and Kozuka, K., Cflow-ad:？Real-time unsupervised anomaly detection with localization？via conditional normalizing flows, Proceedings？of the IEEE/CVF Winter Conference on Applications？of Computer Vision, 2022, pp. 98-107.
Guo, Y., Zeng, Y., Gao, F., Qiu, Y., Zhou, X., Zhong,？L., and Zhan, C., Improved YOLOv4-CSP algorithm？for detection of bamboo surface sliver defects with？extreme aspect ratio, IEEE Access, 2022, Vol. 10, pp. 29810-29820.
Hao, R., Lu, B., Cheng, Y., Li, X., and Huang, B.,？A Steel Surface Defect Inspection Approach Towards？Smart Industrial Monitoring, Journal of Intelligent？Manufacturing, 2021, Vol. 32, pp. 1833-1843.
https://www.mvtec.com/company/research/datasets/mvtec-ad (2022.11.26 access).
Kim, G.N., Kim, S.H., Joo, I., and Yoo, K.H., Detection？of Color Contact Lens Defects using Various CNN？Models, Journal of The Korea Contents Association, 2022, Vol. 22, No. 12, pp. 160-170.
Kim, Y.D., Kim, N.K., and Wang, G.N., Determination？of Defective Products based on DBSCAN using？Temperature Data of Manufacturing Sites, The Korean？Society of Manufacturing Technology Engineers, 2020, pp. 126-126.
Kingma, D.P. and Welling, M., Auto-encoding variational bayes, ArXiv:1312.6114, 2013.
Lugaresi, C., Tang, J., Nash, H., McClanahan, C.,？Uboweja, E., Hays, M., and Grundmann, M., Mediapipe:？A framework for building perception pipelines, ArXiv:1906.08172, 2019.
Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn, and？Bastian Wandt. Asymmetric student-teacher networks？for industrial anomaly detection, Proceedings of the？IEEE/CVF Winter Conference on Applications of Computer？Vision, 2023, pp. 2592-2602.
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., and？Foresti, G.L., VT-ADL: A vision transformer network？for image anomaly detection and localization, IEEE？30th International Symposium on Industrial Electronics (ISIE), 2021, pp. 01-06.
Pang, G., Shen, C., Cao, L., and Hengel, A.V.D., Deep？learning for anomaly detection: A review, ACM？Computing Surveys (CSUR), 2021, Vol. 54, No. 2, pp. 1-38.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A.,？You only look once: Unified, real-time object detection,？Proceedings of the IEEE Conference on Computer Vision？and Pattern Recognition, 2016, pp. 779-788.
Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox,？T., and Gehler, P., Towards total recall in industrial？anomaly detection, Proceedings of the IEEE/CVF？Conference on Computer Vision and Pattern Recognition, 2022, pp. 14318-14328.
Rudolph, M., Wandt, B., and Rosenhahn, B., Same same？but differnet: Semi-supervised defect detection with？normalizing flows, Proceedings of the IEEE/CVF？Winter Conference on Applications of Computer Vision, 2021, pp. 1907-1916.
Son, J.H. and Kim, C.O., A Study on the Application？of Deep Learning Models for Real-time Defect？Detection in the Manufacturing Process - Cases of？Defect detection in the Label Printing Process, Journal？of Korea Technical Association of the Pulp and Paper？Industry, 2021, Vol. 53, No. 5, pp. 74-81.
Um, I.S., Jeong, J.H., and Choi, Y.J., Root Cause？Analysis and Process Condition Optimization of MEA？Manufacturing Systems Using XAI and Bayesian？Optimization, Korean Institute of Industrial Engineers, 2023, pp. 2205-2209.
Wang, G., Han, S., Ding, E., and Huang, D., Student-teacher？feature pyramid matching for anomaly detection, ArXiv:2013.04257, 2021.
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., and？Tang, S., Using Deep Learning to Detect Defects in？Manufacturing: A Comprehensive Survey and Current？Challenges, Materials, 2020, Vol. 13, No. 24, p. 5755.
Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao,？R., and Wu, L., Fastflow: Unsupervised anomaly？detection and localization via 2d normalizing flows, ArXiv:2111.07677, 2021.
Zavrtanik, V., Kristan, M., and Skocaj, D., Draem-a？discriminatively trained reconstruction embedding for？surface anomaly detection, Proceedings of the IEEE/CVF？International Conference on Computer Vision, 2021, pp. 8330-8339.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.