Zobrazeno 1 - 10
of 3 894
pro vyhledávání: '"Surface defect"'
Publikováno v:
Robotic Intelligence and Automation, 2024, Vol. 44, Issue 6, pp. 817-829.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RIA-03-2024-0065
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:
SICE Journal of Control, Measurement, and System Integration, Vol 17, Iss 1, Pp 57-66 (2024)
The safety of railways is ensured by the regular maintenance of tracks, signals and rolling stocks. This mainly involves manual inspection by skilled maintainers and automated inspection systems are not commonly used. The development of an automated
Externí odkaz:
https://doaj.org/article/7c29be96e25840f38f84ce9862fb8d55
Autor:
Chengyu Hu, Jianxin Guo, Hanfei Xie, Qing Zhu, Baoxi Yuan, Yujie Gao, Xiangyang Ma, Jialu Chen
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-28 (2024)
Abstract Compared to the surface defect detection of industrial products produced according to specified processes, the detection of surface defects in naturally grown red jujubes poses unique and significant challenges for researchers. The high dive
Externí odkaz:
https://doaj.org/article/be62f9ec8aa64e3ab6d99f9743611c2a
Publikováno v:
Foundations of Computing and Decision Sciences, Vol 49, Iss 3, Pp 261-285 (2024)
Surface defect detection on wafers is crucial for quality control in semiconductor manufacturing. However, the complexity of defect spatial features, including mixed defect types, large scale differences, and overlapping, results in low detection acc
Externí odkaz:
https://doaj.org/article/ffb2049080734790938cceac72d76b0c
Publikováno v:
Journal of Hebei University of Science and Technology, Vol 45, Iss 4, Pp 351-361 (2024)
To solve the problems of low accuracy, slow detection speed, and difficulty in deploying model parameters in surface defect detection of continuous casting production process, a lightweight surface defect detection algorithm YOLOv7-TSCR that integrat
Externí odkaz:
https://doaj.org/article/02de7239cea34ce9adbc01463658c7e9
Publikováno v:
Chinese Journal of Mechanical Engineering, Vol 37, Iss 1, Pp 1-21 (2024)
Abstract Ensuring high product quality is of paramount importance in pharmaceutical drug manufacturing, as it is subject to rigorous regulatory practices. This study presents a research focused on the development of an on-line detection method and sy
Externí odkaz:
https://doaj.org/article/43c45544f8984d2aad04530d8f404f46
Publikováno v:
Metalurgija, Vol 64, Iss 1-2, Pp 94-96 (2025)
In order to improve the accuracy of surface defect detection of high temperature casting slab, an improved YOLOv5s surface defect detection algorithm is proposed. Firstly, Swin Transformer network structure is added to enhance the ability of feature
Externí odkaz:
https://doaj.org/article/16e67b84de6b4e0ab41c9d7915676c91
Autor:
Fuqin Deng, Jialong Luo, Lanhui Fu, Yonglong Huang, Jianle Chen, Nannan Li, Jiaming Zhong, Tin Lun Lam
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract This article aims to improve the deep-learning-based surface defect recognition. In actual manufacturing processes, there are issues such as data imbalance, insufficient diversity, and poor quality of augmented data in the collected image da
Externí odkaz:
https://doaj.org/article/5c1261b3b6b04c47b2623bca48bedfd5
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting
Externí odkaz:
https://doaj.org/article/3c69bdffb5874501810cac6dcb5d2387