An Unsupervised Online Detection Method for Foreign Objects in Complex Environments

Abstract

In modern industrial production, foreign object detection in complex environments is crucial to ensure product quality and production safety. Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds. To address these issues, this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions, viewing angles, and object scales. The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU). A dataset consisting of complex backgrounds, diverse lighting conditions, and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments. Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC) of 0. 92 and an average F1 score of 0. 85. Combined with data augmentation, the proposed model exhibits improvements in AUROC by 0. 06 and F1 score by 0. 03, demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings. In addition, the effects of key factors on detection performance are systematically analyzed, providing practical guidance for parameter selection in real industrial applications.

Keywords

foreign object detection / unsupervised learning / data augmentation / complex environment / bogie / dataset

References

[1] LI Z W, LIU F, YANG W J, et al. A survey of convolutional neural networks:analysis, applications, and prospects[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12):6999-7019.

[2] HU Z, ZENG X, XIE K, et al. Efficient defect detection of rotating goods under the background of intelligent retail[J]. Sensors, 2024, 24(2):467.

[3] PENG Y, LIU C W, YAN Y, et al. Semi-supervised bolt anomaly detection based on local feature reconstruction[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72:5015812.

[4] PANG G S, SHEN C H, CAO L B, et al. Deep learning for anomaly detection[J]. ACM Computing Surveys, 2022, 54(2):1-38.

[5] BERGMANN P, FAUSER M, SATTLEGGER D, et al. MVTec AD:a comprehensive real-world dataset for unsupervised anomaly detection[C]//2019 IEEE/ CVF Conference on Computer Vision and Pattern Recognition(CVPR). New York: IEEE, 2019:9584-9592.

[6] COHEN N, HOSHEN Y. Sub-image anomaly detection with deep pyramid correspondences[EB/OL].(2020-05-05)[2024-12-23]. https://arxiv.org/abs/2005.02357v3.

[7] DEFARD T, SETKOV A, LOESCH A, et al. PaDiM:a patch distribution modeling framework for anomaly detection and localization[C]// Pattern Recognition. ICPR International Workshops and Challenges. Cham: Springer International Publishing, 2021:475-489.

[8] AKÇAY S, ATAPOUR-ABARGHOUEI A, BRECKON T P. Skip-GANomaly:skip connected and adversarially trained encoder-decoder anomaly detection[C]// 2019 International Joint Conference on Neural Networks(IJCNN). New York: IEEE, 2019:1-8.

[9] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention- MICCAI 2015. Cham: Springer International Publishing, 2015:234-241.

[10] DENG H Q, LI X Y. Anomaly detection via reverse distillation from one-class embedding[C]// 2022 IEEE/ CVF Conference on Computer Vision and Pattern Recognition(CVPR). New York: IEEE, 2022:9727-9736.

[11] GUDOVSKIY D, ISHIZAKA S, KOZUKA K. CFLOW-AD:real-time unsupervised anomaly detection with localization via conditional normalizing flows[C]// 2022 IEEE/ CVF Winter Conference on Applications of Computer Vision(WACV). New York: IEEE, 2022:1819-1828.

[12] ROTH K, PEMULA L, ZEPEDA J, et al. Towards total recall in industrial anomaly detection[C]// 2022 IEEE/ CVF Conference on Computer Vision and Pattern Recognition(CVPR). New York: IEEE, 2022:14298-14308.

[13] ZENG W. Image data augmentation techniques based on deep learning:a survey[J]. Mathematical Biosciences and Engineering, 2024, 21(6):6190-6224.

[14] BAEK K, BANG D, SHIM H. GridMix:strong regularization through local context mapping[J]. Pattern Recognition, 2021, 109:107594.

[15] CUBUK E D, ZOPH B, MANE D, et al. Autoaugment:learning augmentation policies from data[EB/OL].(2019-04-11)[2024-12-23]. https://arxiv.org/abs/1805.09501v3.

[16] SHORTEN C, KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6(1):60.

[17] SALIMANS T, GOODFELLOW I, ZAREMBA W, et al. Improved techniques for training GANs[EB/OL].(2016-06-10)[2025-02-02]. https://arxiv.org/abs/1606.03498v1.

[18] ALOMAR K, AYSEL H I, CAI X H. Data augmentation in classification and segmentation:a survey and new strategies[J]. Journal of Imaging, 2023, 9(2):46.

[19] BOBY R A, SAHA S K. Single image based camera calibration and pose estimation of the end-effector of a robot[C]//2016 IEEE International Conference on Robotics and Automation(ICRA). New York: IEEE, 2016:2435-2440.

[20] FEI-FEI L, DENG J, LI K. ImageNet:Constructing a large-scale image database[J]. Journal of Vision, 2009, 9(8):1037.

[21] LI W, ZHANG Y, SUN Y F, et al. Approximate nearest neighbor search on high dimensional data:experiments, analyses, and improvement[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(8):1475-1488.

[22] Paszke A, Gross S, Massa F, et al. PyTorch:an imperative style, high-performance deep learning library[J]. Advances in Neural Information Processing Systems, 2019, 32.

[23] METZ C E. Basic principles of ROC analysis[J]. Seminars in Nuclear Medicine, 1978, 8(4):283-298.

[24] GOUTTE C, GAUSSIER E. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation[C]//Advances in Information Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005:345-359.

[25] ZAVRTANIK V, KRISTAN M, SKOČAJ D. DRAEM — a discriminatively trained reconstruction embedding for surface anomaly detection[C]//2021 IEEE/ CVF International Conference on Computer Vision(ICCV). New York: IEEE, 2021:8310-8319.

[26] WANG G D, HAN S M, DING E R, et al. Student-teacher feature pyramid matching for anomaly detection[EB/OL].(2021-03-07)[2024-12-23]. https://arxiv.org/abs/2103.04257v3.

[27] TARG S, ALMEIDA D, LYMAN K. Resnet in resnet:generalizing residual architectures[EB/OL].(2016-03-25)[2024-12-23]. https://arxiv.org/abs/1603.08029v1.