Research on distortion correction of particleboard surface defect image

2021 ◽  
Author(s):  
Ziyu Zhao ◽  
Hui Guo ◽  
Xiaoxia Yang ◽  
Zhedong Ge ◽  
Yucheng Zhou
Keyword(s):  
Surface Defect ◽  
Distortion Correction ◽  
Defect Image

Simulation of the Mechanism of Insect Bionic Compound Eye Super-Resolution Restoration Technology of Copper Strip Surface Defect Images

2013 ◽  
Vol 462-463 ◽  
pp. 302-307
Author(s):  
Xiao Dan Sun ◽  
Xin Nan Fan ◽  
Ming Qiang Ling ◽  
Hong Hai Zhuang ◽  
Zhuo Zhang ◽  
...  
Keyword(s):  
Surface Defect ◽  
Compound Eye ◽  
Super Resolution ◽  
Reconstruction Technique ◽  
Copper Strip ◽  
Strip Surface ◽  
Imaging Technology ◽  
Inspection Process ◽  
Defect Image ◽  
Surface Defect Detection

In view of the problem that in traditional copper strip surface defect inspection process, the low resolution of the collected images will greatly decrease the accuracy of the detect defects, through the study of biological bionic imaging technology, combined with insect bionic compound eye imaging technology, this paper proposes a visual simulation insects bionic mechanism of the copper strip surface defect image super-resolution reconstruction technique. Through the study of biological bionic imaging technology, and take the advantage of insect compound eye visual imaging mechanism, this paper uses multiple linear array CCD image sensors to collect images aimed at getting defect images in all perspectives. The actual input images are restored by the method of super-resolution reconstruction using the sample library to improve the resolution of the image. Through the large amount of experiments of different copper strip surface defect images, and then compare the results, it can be seen that taking this papers method to dispose the defect images, will improve the images PSNR value, and has greatly improved the images quality, which will do good to improve the accuracy of the copper strip surface defect detection.

Image Processing of Product Surface Defect Using Scilab

2015 ◽  
Vol 789-790 ◽  
pp. 1223-1226
Author(s):  
Nurfadzylah Awang ◽  
M.H.F.M. Fauadi ◽  
Nurizati Syakirin Rosli
Keyword(s):  
Image Processing ◽  
Surface Defect ◽  
Grayscale Image ◽  
Defect Image ◽  
Product Surface ◽  
Index Terms

— The defect is an imperfection that impairs worth or utility. The defects show some disorder of the product and it is opposite the standard or criteria that have been stated.. In order to define the defect, some techniques are being used. One of the technique is using image processing. The image will be captured by the camera and the image appear will be imported to the Scilab software to read it. Otherwise, the image will be translated into histogram graph to show the frequency (pixel) and grayscale value of the defect.Index Terms— Defect image, surface defect, grayscale image, Scilab software, histogram graph

Identify Steel Ball Surface Defect Based on Combination of Dynamic and Static RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 1000-1004 ◽  
Author(s):  
Yan Ling Zhao ◽  
Feng Ling Wu ◽  
Peng Wang ◽  
Jian Yi Zhang
Keyword(s):  
Neural Network ◽  
Surface Defect ◽  
Layer Structure ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Image Features ◽  
Steel Ball ◽  
Ball Surface ◽  
Defect Image ◽  
Hidden Layer

Steel ball, as a rolling body of all kinds of bearings, it direct affects the bearings precision, dynamic performance and service life. This paper introduces the digital image technology Radial Basis Function (RBF)-Neural network, based on extracting the Steel Ball surface defect image features, used the strategy which is combined with static- dynamic clustering to union the two-stage study and design the hidden layer structure. Simulation and experiment show that the RBF-neural network runs stably, has fast convergence and overall accuracy rate of 96%. These can meet the needs of practical application.

scholarly journals Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

2018 ◽  
Vol 8 (9) ◽  
pp. 1575 ◽  
Author(s):  
Xian Tao ◽  
Dapeng Zhang ◽  
Wenzhi Ma ◽  
Xilong Liu ◽  
De Xu
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
Low Noise ◽  
Machine Learning Techniques ◽  
Metallic Surface ◽  
Defect Inspection ◽  
Learning Techniques ◽  
Defect Image ◽  
Surface Defect Detection

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

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