Rail Surface Damage Detection Method Based on Improved U-Net Convolutional Neural Network

2021 ◽  
Vol 58 (2) ◽  
pp. 0215009
Author(s):  
梁波 Liang Bo ◽  
卢军 Lu Jun ◽  
曹阳 Cao Yang
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Convolutional Neural Network ◽  
Detection Method ◽  
Surface Damage

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Saliency Contour Extraction Based on Multi-level Feature Channel Optimization Coding

2021 ◽  
Vol 7 (4) ◽  
pp. 117
Author(s):  
Linling Fang ◽  
Yingle Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Detection Method ◽  
Gabor Filter ◽  
Contour Detection ◽  
Frequency Separation ◽  
Experimental Sample ◽  
Multi Level ◽  
End To End

<p>A biomimetic vision computing model based on multi-level feature channel optimization coding is proposed and applied to image contour detection, combining the end-to-end detection method of full convolutional neural network and the traditional contour detection method based on biological vision mechanism. Considering the effectiveness of the Gabor filter in perceiving the scale and direction of the image target, the Gabor filter is introduced to simulate the multi-level feature response on the visual path. The optimal scale and direction of the Gabor filter are obtained based on the similarity index, and they are used as the frequency separation parameter of the NSCT transform. The contour sub-image obtained by the NSCT transform is combined with the original image for feature enhancement and fusion to realize the primary contour response. The low-dimensional and low-redundancy primary contour response is used as the input sample of the network model to relieve network pressure and reduce computational complexity. A fully improved convolutional neural network model is constructed for multi-scale training, through feature encoder to feature decoder, to achieve end-to-end pixel prediction, and obtain a complete and continuous detection image of the subject contour. Using the BSDS500 atlas as the experimental sample, the average accuracy index is 0.85, which runs on the device CPU at a detection rate of 20+ FPS to achieve a good balance between training efficiency and detection effect.</p>

A real-time music detection method based on convolutional neural network using Mel-spectrogram and spectral flux

2021 ◽  
Vol 263 (1) ◽  
pp. 5910-5918
Author(s):  
Yiya Hao ◽  
Yaobin Chen ◽  
Weiwei Zhang ◽  
Gong Chen ◽  
Liang Ruan
Keyword(s):  
Neural Network ◽  
Online Education ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Speech Intelligibility ◽  
Classical Music ◽  
Detection Method ◽  
Automatic Gain Control ◽  
Real Time Processing ◽  
Spectral Flux

Audio processing, including speech enhancement system, improves speech intelligibility and quality in real-time communication (RTC) such as online meetings and online education. However, such processing, primarily noise suppression and automatic gain control, is harmful to music quality when the captured signal is music instead of speech. A music detector can solve the issue above by switching off the speech processing when the music is detected. In RTC scenarios, the music detector should be low-complexity and cover various situations, including different types of music, background noises, and other acoustical environments. In this paper, a real-time music detection method with low-computation complexity is proposed, based on a convolutional neural network (CNN) using Mel-spectrogram and spectral flux as input features. The proposed method achieves overall 90.63% accuracy under different music types (classical music, instruments solos, singing-songs, etc.), speech languages (English and Mandarin), and noise types. The proposed method is constructed on a lightweight CNN model with a small feature size, which guarantees real-time processing.

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