scholarly journals Surface Flaw Detection of Industrial Products Based on Convolutional Neural Network

2019 ◽  
Vol 252 ◽  
pp. 022114 ◽  
Author(s):  
Yongjun Zhang ◽  
Ziliang Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Flaw Detection ◽  
Surface Flaw ◽  
Industrial Products

scholarly journals A robust approach for industrial small-object detection using an improved faster regional convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Faisal Saeed ◽  
Muhammad Jamal Ahmed ◽  
Malik Junaid Gul ◽  
Kim Jeong Hong ◽  
Anand Paul ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Industrial Sector ◽  
Small Object ◽  
Industrial Products ◽  
Amplification Method ◽  
Small Object Detection ◽  
Improved Model

AbstractWith the increasing pace in the industrial sector, the need for a smart environment is also increasing and the production of industrial products in terms of quality always matters. There is a strong burden on the industrial environment to continue to reduce impulsive downtime, concert deprivation, and safety risks, which needs an efficient solution to detect and improve potential obligations as soon as possible. The systems working in industrial environments for generating industrial products are very fast and generate products rapidly, sometimes leading to faulty products. Therefore, this problem needs to be solved efficiently. Considering this problem in terms of faulty small-object detection, this study proposed an improved faster regional convolutional neural network-based model to detect the faults in the product images. We introduced a novel data-augmentation method along with a bi-cubic interpolation-based feature amplification method. A center loss is also introduced in the loss function to decrease the inter-class similarity issue. The experimental results show that the proposed improved model achieved better classification accuracy for detecting our small faulty objects. The proposed model performs better than the state-of-the-art methods.

scholarly journals Fully Convolutional Neural Network With GRU for 3D Braided Composite Material Flaw Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 151180-151188 ◽  
Author(s):  
Yongmin Guo ◽  
Zhitao Xiao ◽  
Lei Geng ◽  
Jun Wu ◽  
Fang Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Composite Material ◽  
Convolutional Neural Network ◽  
Flaw Detection ◽  
Braided Composite

scholarly journals Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network

2021 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Diny Melsye Nurul Fajri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Environmentally Friendly ◽  
Machine Learning Method ◽  
Learning Method ◽  
Industrial Products ◽  
Kenaf Fiber ◽  
Plant Pests

Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.

Quantitative weld defect sizing using convolutional neural network-aided processing of RT images

2021 ◽  
Vol 63 (3) ◽  
pp. 141-145
Author(s):  
M Mirzapour ◽  
A Movafeghi ◽  
E Yahaghi
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Flaw Detection ◽  
Flaw Size ◽  
Image Feature ◽  
Image Processing Technique ◽  
Radiographic Images ◽  
Weld Defect ◽  
Image Contrast Enhancement

Non-destructive confirmation of seamless welding is of critical importance in most applications and digital industrial radiography (DIR) is often the method of choice for internal flaw detection. DIR images often suffer from fogginess, limiting the inspection of flawed regions in online and quantitative applications. Much focus has therefore been put on denoising and image fog removal to yield better outcomes. One of the methods most widely used to improve the image is the fast and flexible denoising convolutional neural network (FFCN). This method has been shown to offer excellent image quality performance combined with fast execution and computing efficiency. In this study, the FFCN image processing technique is implemented and applied to radiographic images of welded objects. Enhancement of defect detection is achieved through sharpening of the image feature edges, leading to improved quantification in weld flaw sizing. The method is applied to the radiographic images using the weighted subtraction method. Experienced radiographers find that the weld defect detail is better visualised with output images from the FFCN algorithm compared to the original radiographs. Improvement in weld flaw size quantification is evaluated using test objects and the distance between the first two lines of the image quality indicator (IQI). The results show that the applied algorithm enhances the visualisation of internal defects and increases the detectability of fine fractures in the welded region. It is also found that, by selective image contrast enhancement near the flaw edges, flaw size quantification is improved significantly. The algorithm is found to be efficient, enabling online automated implementation on standard personal computers.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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