Surface Crack Detection Algorithm for Nuclear Fuel Pellets

2019 ◽  
Vol 56 (16) ◽  
pp. 161008
Author(s):  
宋文豪 Wenhao Song ◽  
张斌 Bin Zhang ◽  
李峰宇 Fengyu Li ◽  
杨腾达 Tengda Yang ◽  
李建宁 Jianning Li ◽  
...  
Keyword(s):  
Nuclear Fuel ◽  
Surface Crack ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Fuel Pellets ◽  
Nuclear Fuel Pellets

A robust self-driven surface crack detection algorithm using local features

2020 ◽  
Vol 62 (5) ◽  
pp. 269-276 ◽  
Author(s):  
Aixi Zhu ◽  
Yiming Zhu ◽  
Nizhuan Wang ◽  
Yingying Chen
Keyword(s):  
Surface Crack ◽  
Crack Detection ◽  
Building Materials ◽  
Detection Method ◽  
Detection Algorithm ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Local Texture ◽  
Original Photograph ◽  
Sparse Features

This paper presents an effective image analysis method for visual surface crack detection, called a robust self-driven crack detection algorithm (RSCDA). Firstly, a local texture anisotropy (LTA) is estimated based on self-driven local feature statistics from the original photograph. Secondly, the LTA is used to detect candidate crack pixels. Finally, the actual crack pixels are accurately identified using two effective measurements for connected domains based on discriminative direction and relative sparse features. The results demonstrate that the RSCDA is an effective and robust surface crack detection method for building materials or textiles.

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