دورية أكاديمية
P2 random walk: self-supervised anomaly detection with pixel-point random walk
| العنوان: | P2 random walk: self-supervised anomaly detection with pixel-point random walk |
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| المؤلفون: | Liujie Hua, Qianqian Qi, Jun Long |
| المصدر: | Complex & Intelligent Systems, Vol 10, Iss 2, Pp 2541-2555 (2023) |
| بيانات النشر: | Springer, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Information technology |
| مصطلحات موضوعية: | Anomaly detection, Data augmentation, Intelligent manufacturing, Self-supervised learning, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64 |
| الوصف: | Abstract In the domain of intelligent manufacturing, automatic anomaly detection plays a pivotal role and holds great significance for improving production efficiency and product quality. However, the scarcity and uncertainty of anomalous data pose significant challenges in this field. Data augmentation methods, such as Cutout, which are widely adopted in existing methodologies, tend to generate patterned data, leading to biased data and compromised detection performance. To deal with this issue, we propose an approach termed self-supervised anomaly detection with pixel-point random walk (P2 Random Walk), which combines data augmentation and Siamese neural networks. We develop a pixel-level data augmentation technique to enhance the randomness of generated data and establish a two-stage anomaly classification framework. The effectiveness of the P2 Random Walk method has been demonstrated on the MVTec dataset, achieving an AUROC of 96.2% and 96.3% for classification and segmentation, respectively, by using only data augmentation-based techniques. Specifically, our method outperforms other state-of-the-art methods in several categories, improving the AUROC for classification and segmentation by 0.5% and 0.3%, respectively, which demonstrates the high performance and strong academic value of our method in anomaly detection tasks. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2199-4536 2198-6053 |
| Relation: | https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053 |
| DOI: | 10.1007/s40747-023-01285-z |
| URL الوصول: | https://doaj.org/article/624ed3b46ccf4cf294474ee8d8e72e23 |
| رقم الأكسشن: | edsdoj.624ed3b46ccf4cf294474ee8d8e72e23 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 21994536 21986053 |
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| DOI: | 10.1007/s40747-023-01285-z |