Automated Defect Recognition of Castings Defects Using Neural Networks

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
A. García Pérez ◽  
M. J. Gómez Silva ◽  
A. de la Escalera Hueso
Keyword(s):  
Neural Networks ◽  
Defect Recognition

Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Dhruv Gamdha ◽  
Sreedhar Unnikrishnakurup ◽  
K. J. Jyothir Rose ◽  
M. Surekha ◽  
Padma Purushothaman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Solid Propellant ◽  
X Ray ◽  
Defect Recognition

scholarly journals Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1176
Author(s):  
Aleksei Boikov ◽  
Vladimir Payor ◽  
Roman Savelev ◽  
Alexandr Kolesnikov
Keyword(s):  
Neural Networks ◽  
Defect Detection ◽  
Production Control ◽  
Surface Defects ◽  
Synthetic Data ◽  
Real Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Steel Slab ◽  
Defect Recognition

The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

scholarly journals Fabric defect recognition using optimized neural networks

2019 ◽  
Vol 14 ◽  
pp. 155892501989739 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chi Zhang ◽  
Chunlei Li ◽  
Shumin Ding ◽  
Yan Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recognition Task ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fabric Defect ◽  
Defect Recognition

Fabric defect recognition is an important measure for quality control in a textile factory. This article utilizes a deep convolutional neural network to recognize defects in fabrics that have complicated textures. Although convolutional neural networks are very powerful, a large number of parameters consume considerable computation time and memory bandwidth. In real-world applications, however, the fabric defect recognition task needs to be carried out in a timely fashion on a computation-limited platform. To optimize a deep convolutional neural network, a novel method is introduced to reveal the input pattern that originally caused a specific activation in the network feature maps. Using this visualization technique, this study visualizes the features in a fully trained convolutional model and attempts to change the architecture of original neural network to reduce computational load. After a series of improvements, a new convolutional network is acquired that is more efficient to the fabric image feature extraction, and the computation load and the total number of parameters in the new network is 23% and 8.9%, respectively, of the original model. The proposed neural network is specifically tailored for fabric defect recognition in resource-constrained environments. All of the source code and pretrained models are available online at https://github.com/ZCmeteor .

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