Defect detection of roller surfaces based on FPGA image processing

2016 ◽  
Author(s):  
S. Save ◽  
K. Jadhav
Keyword(s):  
Image Processing ◽  
Defect Detection

scholarly journals Research on Surface Defect Detection of Aluminum based on Image Processing

2020 ◽  
Vol 25 ◽  
pp. 822-825
Author(s):  
Xuemin Liu ◽  
Ce Bian ◽  
Di Yin ◽  
Yuxuan Zhu ◽  
Yasheng Yuan ◽  
...  
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

scholarly journals Defect Detection in Striped Images Using a One-Dimensional Median Filter

2020 ◽  
Vol 10 (3) ◽  
pp. 1012
Author(s):  
Wei-Chen Lee ◽  
Pei-Ling Tai
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
Median Filter ◽  
Detection Methods ◽  
Manufacturing Processes ◽  
Image Binarization ◽  
Image Processing Algorithm ◽  
One Dimensional ◽  
Modern Manufacturing ◽  
The Difference

Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.

705 Development of defect detection system by image processing using wavelet transform

2010 ◽  
Vol 2010.18 (0) ◽  
pp. 185-186
Author(s):  
Kaoru TAKAMORI ◽  
Masashi ONO ◽  
Kazutaka NONOMURA ◽  
Libo ZHOU ◽  
Hirotaka OJIMA
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Defect Detection ◽  
Detection System
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