scholarly journals Embedded system for road damage detection by deep convolutional neural network

2019 ◽  
Vol 16 (6) ◽  
pp. 7982-7994
Author(s):  
Siyu Chen ◽  
◽  
Yin Zhang ◽  
Yuhang Zhang ◽  
Jiajia Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network

Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning

2019 ◽  
Vol 23 (10) ◽  
pp. 4493-4502 ◽  
Author(s):  
Chuncheng Feng ◽  
Hua Zhang ◽  
Shuang Wang ◽  
Yonglong Li ◽  
Haoran Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Structural Damage ◽  
Deep Convolutional Neural Network ◽  
Structural Damage Detection

scholarly journals Damage Detection With an Ultrasound Array and Deep Convolutional Neural Network Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 189423-189435
Author(s):  
Donggeun Kim ◽  
San Kim ◽  
Siheon Jeong ◽  
Ji-Wan Ham ◽  
Seho Son ◽  
...  
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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