High-speed fringe projection profilometry based on convolutional neural network

2021 ◽  
Author(s):  
Jiaye Wang ◽  
Yuzhen Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Fringe Projection ◽  
Fringe Projection Profilometry

A fully automated method of human identification based on dental panoramic radiographs using a convolutional neural network

2021 ◽  
Author(s):  
Young Hyun Kim ◽  
Eun-Gyu Ha ◽  
Kug Jin Jeon ◽  
Chena Lee ◽  
Sang-Sun Han
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Large Scale ◽  
Oral Surgery ◽  
Human Identification ◽  
Running Time ◽  
Automated Method ◽  
Image Characteristics ◽  
Proposed Model

Objectives: This study aimed to develop a fully automated human identification method based on a convolutional neural network (CNN) with a large-scale dental panoramic radiograph (DPR) dataset. Methods: In total, 2,760 DPRs from 746 subjects who had 2 to 17 DPRs with various changes in image characteristics due to various dental treatments (tooth extraction, oral surgery, prosthetics, orthodontics, or tooth development) were collected. The test dataset included the latest DPR of each subject (746 images) and the other DPRs (2,014 images) were used for model training. A modified VGG16 model with two fully connected layers was applied for human identification. The proposed model was evaluated with rank-1, –3, and −5 accuracies, running time, and gradient-weighted class activation mapping (Grad-CAM)–applied images. Results: This model had rank-1,–3, and −5 accuracies of 82.84%, 89.14%, and 92.23%, respectively. All rank-1 accuracy values of the proposed model were above 80% regardless of changes in image characteristics. The average running time to train the proposed model was 60.9 sec per epoch, and the prediction time for 746 test DPRs was short (3.2 sec/image). The Grad-CAM technique verified that the model automatically identified humans by focusing on identifiable dental information. Conclusion: The proposed model showed good performance in fully automatic human identification despite differing image characteristics of DPRs acquired from the same patients. Our model is expected to assist in the fast and accurate identification by experts by comparing large amounts of images and proposing identification candidates at high speed.

scholarly journals Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 655
Author(s):  
Benedikt Adelmann ◽  
Max Schleier ◽  
Ralf Hellmann
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Error Rate ◽  
High Speed ◽  
Sheet Thickness ◽  
Error Rates ◽  
Training Set ◽  
Color Changes ◽  
Steel Sheets ◽  
Additional Illumination

In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets of varying thicknesses with different laser parameter combinations and classifies them into cuts and cut interruptions. After a short learning period of five epochs on a certain sheet thickness, the images are classified with a low error rate of 0.05%. The use of color images reveals slight advantages with lower error rates over greyscale images, since, during cut interruptions, the image color changes towards blue. A training set on all sheet thicknesses in one network results in tests error rates below 0.1%. This low error rate and the short calculation time of 120 µs on a standard CPU makes the system industrially applicable.

FPGA Design of High-Speed Convolutional Neural Network Hardware Accelerator

2021 ◽  
Author(s):  
Ahmed J. Abd El-Maksoud ◽  
Abdallah Mohamed ◽  
Ahmed Tarek ◽  
Amr Adel ◽  
Amr Eid ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Hardware Accelerator ◽  
Fpga Design ◽  
Neural Network Hardware

scholarly journals A Novel Methodology for Series Arc Fault Detection by Temporal Domain Visualization and Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 162 ◽  
Author(s):  
Kai Yang ◽  
Ruobo Chu ◽  
Rencheng Zhang ◽  
Jinchao Xiao ◽  
Ran Tu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Current Transformer ◽  
Desktop Computer ◽  
Temporal Domain ◽  
High Speed Data Acquisition ◽  
Ac Arc ◽  
Electrical Wiring

AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed. The current transformer and high-speed data acquisition system are used to collect the current of a series of arc faults, then the signal is filtered by a digital filter and converted into a gray image in time sequence before being fed into TDV-CNN. Five different electric loads were selected for experimental validation with various signal characteristics, including vacuum cleaner, fluorescent lamp, dimmer, heater, and desktop computer. The experimental results confirm that the classification accuracy of the five loads’ work states in the ten categories could reach 98.7% or even higher by adjusting parameters perfectly. The methodology is believed to be reliable for series arc detection with relatively high accuracy and also has important potential applications in other fault diagnosis fields.

Sign in / Sign up

Export Citation Format

Share Document