Zobrazeno 1 - 10
of 54 041
pro vyhledávání: '"surface defect"'
We study an $O(N)$ invariant surface defect in the Wilson-Fisher conformal field theory (CFT) in $d=4-\epsilon$ dimensions. This defect is defined by mass deformation on a two-dimensional surface that generates localized disorder and is conjectured t
Externí odkaz:
http://arxiv.org/abs/2411.16522
Current saliency-based defect detection methods show promise in industrial settings, but the unpredictability of defects in steel production environments complicates dataset creation, hampering model performance. Existing data augmentation approaches
Externí odkaz:
http://arxiv.org/abs/2412.15570
Publikováno v:
China Forest Products Industry. Nov2024, Vol. 61 Issue 11, p6-32. 9p.
Autor:
Yu, Jun, Wang, WenJian
Recycled and recirculated books, such as ancient texts and reused textbooks, hold significant value in the second-hand goods market, with their worth largely dependent on surface preservation. However, accurately assessing surface defects is challeng
Externí odkaz:
http://arxiv.org/abs/2409.04958
Surface defect detection is significant in industrial production. However, detecting defects with varying textures and anomaly classes during the test time is challenging. This arises due to the differences in data distributions between source and ta
Externí odkaz:
http://arxiv.org/abs/2408.09494
Autor:
Chan, Sixian1 (AUTHOR), Li, Suqiang2 (AUTHOR), Zhang, Hongkai2 (AUTHOR), Zhou, Xiaolong3 (AUTHOR) xiaolong@ieee.org, Mao, Jiafa1 (AUTHOR), Hong, Feng4 (AUTHOR) hongfeng@zjsru.edu.cn
Publikováno v:
Scientific Reports. 12/30/2024, Vol. 14 Issue 1, p1-15. 15p.
The challenge of data scarcity hinders the application of deep learning in industrial surface defect classification (SDC), as it's difficult to collect and centralize sufficient training data from various entities in Industrial Internet of Things (II
Externí odkaz:
http://arxiv.org/abs/2409.15711
The aim of surface defect detection is to identify and localise abnormal regions on the surfaces of captured objects, a task that's increasingly demanded across various industries. Current approaches frequently fail to fulfil the extensive demands of
Externí odkaz:
http://arxiv.org/abs/2408.03143
Autor:
Shuangning Liu, Junfeng Li
Publikováno v:
Complex & Intelligent Systems, Vol 11, Iss 1, Pp 1-19 (2024)
Abstract In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion N
Externí odkaz:
https://doaj.org/article/5b10a0bce210425082aa0b50549a4f7d
Publikováno v:
Metalurgija. 2025, Vol. 64 Issue 1/2, p94-96. 3p.