Background texture suppression and defect extraction of high-speed milling surface image

Zhou, You Hang; Li, Yong; Shi, Xian Wei; Kong, Tuo; Liu, Shu

doi:10.1504/ijmms.2018.095461

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results show that the algorithm is effective and does not need a priori condition. Zhou et al [29] proposed a method to extract machine tool defects from high-speed milling workpiece surface images, which reduces the influence of workpiece surface background texture. The application example shows that this method can effectively extract machine tool defects.…”

Section: Introductionmentioning

confidence: 99%

Accurate Detection for Zirconium Sheet Surface Scratches Based on Visible Light Images

Xu,

Sun,

et al. 2023

Sensors

View full text Add to dashboard Cite

Zirconium sheet has been widely used in various fields, e.g., chemistry and aerospace. The surface scratches on the zirconium sheets caused by complex processing environment have a negative impact on the performance, e.g., working life and fatigue fracture resistance. Therefore, it is necessary to detect the defect of zirconium sheets. However, it is difficult to detect such scratch images due to lots of scattered additive noise and complex interlaced structural texture. Hence, we propose a framework for adaptively detecting scratches on the surface images of zirconium sheets, including noise removing and texture suppressing. First, the noise removal algorithm, i.e., an optimized threshold function based on dual-tree complex wavelet transform, uses selected parameters to remove scattered and numerous noise. Second, the texture suppression algorithm, i.e., an optimized relative total variation enhancement model, employs selected parameters to suppress interlaced texture. Finally, by connecting disconnection based on two types of connection algorithms and replacing the Gaussian filter in the standard Canny edge detection algorithm with our proposed framework, we can more robustly detect the scratches. The experimental results show that the proposed framework is of higher accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%