scholarly journals Research on Driver Status Recognition System of Intelligent Vehicle Terminal Based on Deep Learning

2021 ◽  
Vol 12 (3) ◽  
pp. 137
Author(s):  
Yiming Xu ◽  
Wei Peng ◽  
Li Wang
Keyword(s):  
Three Dimensional ◽  
Recognition System ◽  
Identification Accuracy ◽  
Research Progress ◽  
Video Frame ◽  
Safe Driving ◽  
State Recognition ◽  
Phone Calls ◽  
Spatio Temporal ◽  
Status Recognition

Automobile safety driving technology is a hot topic in today’s society, which is very significant to the social transportation system. Vehicle driving behavior monitoring is the foundation and core of safe driving techniques. The research on existing vehicle safety technology can not only improve the understanding of current safe driving research progress, but also provide reference for future researchers. This paper proposes a state recognition system based on a three-dimensional convolutional neural network, which can identify several improper states frequently encountered by drivers during driving, including drinking, making phone calls, and smoking, and can also issue alarm interventions. The system takes the collected continuous video frame information as the input of the three-dimensional convolutional network, carries out multi-level feature extraction and spatio-temporal information fusion, and identifies the driver state according to the extracted spatio-temporal features. The state is judged by the facial feature points of the video stream, and the design of the video surveillance driver state recognition system is completed. Then, the driver status recognition is improved and optimized, and finally, the actual deployment of the driver status recognition system on the mobile terminal is completed. A large number of experimental results show that the driver status recognition system proposed in this paper has achieved upper identification accuracy.

A Study on Utilization of Three-Dimensional Sensor Lip Image for Developing a Pronunciation Recognition System

2019 ◽  
Vol 63 (5) ◽  
pp. 50402-1-50402-9 ◽  
Author(s):  
Ing-Jr Ding ◽  
Chong-Min Ruan
Keyword(s):  
Principal Component Analysis ◽  
Automatic Speech Recognition ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Principal Component ◽  
Recognition System ◽  
Geometrical Characteristics ◽  
3D Geometry ◽  
Different Types ◽  
The Disabled

Abstract The acoustic-based automatic speech recognition (ASR) technique has been a matured technique and widely seen to be used in numerous applications. However, acoustic-based ASR will not maintain a standard performance for the disabled group with an abnormal face, that is atypical eye or mouth geometrical characteristics. For governing this problem, this article develops a three-dimensional (3D) sensor lip image based pronunciation recognition system where the 3D sensor is efficiently used to acquire the action variations of the lip shapes of the pronunciation action from a speaker. In this work, two different types of 3D lip features for pronunciation recognition are presented, 3D-(x, y, z) coordinate lip feature and 3D geometry lip feature parameters. For the 3D-(x, y, z) coordinate lip feature design, 18 location points, each of which has 3D-sized coordinates, around the outer and inner lips are properly defined. In the design of 3D geometry lip features, eight types of features considering the geometrical space characteristics of the inner lip are developed. In addition, feature fusion to combine both 3D-(x, y, z) coordinate and 3D geometry lip features is further considered. The presented 3D sensor lip image based feature evaluated the performance and effectiveness using the principal component analysis based classification calculation approach. Experimental results on pronunciation recognition of two different datasets, Mandarin syllables and Mandarin phrases, demonstrate the competitive performance of the presented 3D sensor lip image based pronunciation recognition system.

scholarly journals Single-shot detection of 8 unique monochrome fringe patterns representing 4 distinct directions via multispectral fringe projection profilometry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Parsa Omidi ◽  
Mohamadreza Najiminaini ◽  
Mamadou Diop ◽  
Jeffrey J. L. Carson
Keyword(s):  
Spatial Resolution ◽  
Fringe Pattern ◽  
Three Dimensional ◽  
Fringe Projection ◽  
Video Frame ◽  
Single Shot ◽  
Shot Detection ◽  
Fringe Patterns ◽  
Fringe Projection Profilometry ◽  
Multispectral Camera

AbstractSpatial resolution in three-dimensional fringe projection profilometry is determined in large part by the number and spacing of fringes projected onto an object. Due to the intensity-based nature of fringe projection profilometry, fringe patterns must be generated in succession, which is time-consuming. As a result, the surface features of highly dynamic objects are difficult to measure. Here, we introduce multispectral fringe projection profilometry, a novel method that utilizes multispectral illumination to project a multispectral fringe pattern onto an object combined with a multispectral camera to detect the deformation of the fringe patterns due to the object. The multispectral camera enables the detection of 8 unique monochrome fringe patterns representing 4 distinct directions in a single snapshot. Furthermore, for each direction, the camera detects two π-phase shifted fringe patterns. Each pair of fringe patterns can be differenced to generate a differential fringe pattern that corrects for illumination offsets and mitigates the effects of glare from highly reflective surfaces. The new multispectral method solves many practical problems related to conventional fringe projection profilometry and doubles the effective spatial resolution. The method is suitable for high-quality fast 3D profilometry at video frame rates.

Symbol recognition system based on 3D stereo vision

2020 ◽  
pp. 1-10
Author(s):  
Linlin Wang
Keyword(s):  
Camera Calibration ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Recognition System ◽  
Symbol Recognition ◽  
Two Dimensional ◽  
Measuring Machine ◽  
Left And Right

With the continuous development of computer science and technology, symbol recognition systems may be converted from two-dimensional space to three-dimensional space. Therefore, this article mainly introduces the symbol recognition system based on 3D stereo vision. The three-dimensional image is taken by the visual coordinate measuring machine in two places on the left and right. Perform binocular stereo matching on the edge of the feature points of the two images. A corner detection algorithm combining SUSAN and Harris is used to detect the left and right camera calibration templates. The two-dimensional coordinate points of the object are determined by the image stereo matching module, and the three-dimensional discrete coordinate points of the object space can be obtained according to the transformation relationship between the image coordinates and the actual object coordinates. Then draw the three-dimensional model of the object through the three-dimensional drawing software. Experimental data shows that the logic resources and memory resources occupied by image preprocessing account for 30.4% and 27.4% of the entire system, respectively. The results show that the system can calibrate the internal and external parameters of the camera. In this way, the camera calibration result will be more accurate and the range will be wider. At the same time, it can effectively make up for the shortcomings of traditional modeling techniques to ensure the measurement accuracy of the detection system.

Research Progress and Application of Modified Silicon-Based Anode Materials for Lithium-Ion Batteries

2021 ◽  
Vol 1036 ◽  
pp. 35-44
Author(s):  
Ling Fang Ruan ◽  
Jia Wei Wang ◽  
Shao Ming Ying
Keyword(s):  
Electrochemical Performance ◽  
Lithium Ion Batteries ◽  
Volume Expansion ◽  
Anode Materials ◽  
Active Material ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Research Progress ◽  
Ion Intercalation ◽  
Silicon Based

Silicon-based anode materials have been widely discussed by researchers because of its high theoretical capacity, abundant resources and low working voltage platform,which has been considered to be the most promising anode materials for lithium-ion batteries. However,there are some problems existing in the silicon-based anode materials greatly limit its wide application: during the process of charge/discharge, the materials are prone to about 300% volume expansion, which will resultin huge stress-strain and crushing or collapse on the anods; in the process of lithium removal, there is some reaction between active material and current collector, which creat an increase in the thickness of the solid phase electrolytic layer(SEI film); during charging and discharging, with the increase of cycle times, cracks will appear on the surface of silicon-based anode materials, which will cause the batteries life to decline. In order to solve these problems, firstly, we summarize the design of porous structure of nanometer sized silicon-based materials and focus on the construction of three-dimensional structural silicon-based materials, which using natural biomass, nanoporous carbon and metal organic framework as structural template. The three-dimensional structure not only increases the channel of lithium-ion intercalation and the rate of ion intercalation, but also makes the structure more stable than one-dimensional or two-dimensional. Secondly, the Si/C composite, SiOx composite and alloying treatment can improve the volume expansion effection, increase the rate of lithium-ion deblocking and optimize the electrochemical performance of the material. The composite materials are usually coated with elastic conductive materials on the surface to reduce the stress, increase the conductivity and improve the electrochemical performance. Finally, the future research direction of silicon-based anode materials is prospected.

scholarly journals Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5136
Author(s):  
Xiaoxin Fang ◽  
Qiwu Luo ◽  
Bingxing Zhou ◽  
Congcong Li ◽  
Lu Tian
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Three Dimensional ◽  
Image Features ◽  
Stereoscopic Vision ◽  
Research Progress ◽  
Inspection System ◽  
Two Dimensional ◽  
Surface Defect Detection ◽  
Planar Materials

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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