Tunnel Emergence Detection Technology based on Hybrid Image Recognition

2021 ◽  
Vol 19 (12) ◽  
pp. 11-18
Author(s):  
Seojeong Kim ◽  
Sunghwan Jeong ◽  
Keunho Park ◽  
Donghoon Kim ◽  
Cheol-Jung Yoo ◽  
...  
Keyword(s):  
Image Recognition ◽  
Detection Technology

Design of Outdoor Fire Intelligent Alarm System Based on Image Recognition

2019 ◽  
Vol 34 (07) ◽  
pp. 2050018
Author(s):  
Kun Zhou ◽  
Xi Zhang
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Low Cost ◽  
Fire Detection ◽  
Human Society ◽  
Alarm System ◽  
Training Algorithms ◽  
Fire Control ◽  
Detection Technology ◽  
Control Work

Fire is one of the most common serious disasters in human society. It is a kind of burning phenomenon that is out of control in time and space. When a fire occurs, how to detect the fire quickly and remove it in the budding state has become the key content of fire control work. Outdoor fire is very common in our daily life, and once it occurs without effective and timely control, it will cause huge losses. Therefore, it is particularly important to study an intelligent alarm system for outdoor fire. Generally, fire detection technology can be divided into sensor fire detection technology and image fire detection technology. Sensor fire detection technology is low cost and easy to design, but its application field is limited. Under the interference of many factors outside, misjudgement and missed judgement will occur. Image fire detection technology can achieve certain detection function through manual design of features and classifiers, but there are still defects in the application in the actual diversified environment. With the development of neural network technology in recent years, it has made great breakthroughs in the field of image recognition. Its judgment type is obtained through a large number of data training algorithms. Because of its automatic feature extraction and classification characteristics, it can effectively adapt to the external environment. Therefore, this paper proposes an end-to-end two-stream neural network model to detect fires, uses fire video on the network to train the algorithm, and then uses the fire database to test. Compared with the existing fire detection algorithms, it is found that the proposed method has good practicability and versatility, and provides a good reference for the development of fire detection technology.

scholarly journals Three dimensional acoustic imaging technology of buried object detection

2019 ◽  
Vol 283 ◽  
pp. 04010
Author(s):  
Weihua Cong ◽  
Lisheng Zhou
Keyword(s):  
Object Detection ◽  
Image Recognition ◽  
3D Imaging ◽  
Three Dimensional ◽  
Acoustic Imaging ◽  
Imaging Method ◽  
Imaging Technology ◽  
Buried Objects ◽  
Detection Technology ◽  
Buried Object

With the development of 21th century seabed imaging sonar technology, more and more attention is paid to buried object detection technology in the world. In this paper, a low frequency and high resolution three-dimensional acoustic imaging of buried object detection method and its application example are given. Compared with the traditional two-dimensional synthetic aperture imaging, the 3D imaging technology not only solves the problem of the aliasing of the seabed formation echo and the sea floor echo, being able to provide the target buried depth, but also the 3D imaging is more helpful to the image recognition. The 3D acoustic imaging method proposed by this paper has already become the development trend of buried object detection technology. We have noticed that, different from the three-dimensional visualization of the target in the water, the three-dimensional visualization of buried objects has a serious formation image occlusion problem. In addition, the three-dimensional imaging needs to be obtained centimeter-level resolution on three dimensions for better image recognition of small buried objects, in which azimuth resolution is the bottleneck.

On-line image readout in CTEM

1994 ◽  
Vol 52 ◽  
pp. 400-401
Author(s):  
K.-H. Herrmann ◽  
W. D. Rau ◽  
R. Sikeler
Keyword(s):  
Primary Electron ◽  
Digital Information ◽  
Electron Hole ◽  
Detection Technology ◽  
Long Time ◽  
On Line ◽  
Electron Image ◽  
Optical Transfer ◽  
Detection Quantum Efficiency ◽  
Technical Solutions

Quantitative recording of electron patterns and their rapid conversion into digital information is an outstanding goal which the photoplate fails to solve satisfactorily. For a long time, LLL-TV cameras have been used for EM adjustment but due to their inferior pixel number they were never a real alternative to the photoplate. This situation has changed with the availability of scientific grade slow-scan charged coupled devices (CCD) with pixel numbers exceeding 106, photometric accuracy and, by Peltier cooling, both excellent storage and noise figures previously inaccessible in image detection technology. Again the electron image is converted into a photon image fed to the CCD by some light optical transfer link. Subsequently, some technical solutions are discussed using the detection quantum efficiency (DQE), resolution, pixel number and exposure range as figures of merit.A key quantity is the number of electron-hole pairs released in the CCD sensor by a single primary electron (PE) which can be estimated from the energy deposit ΔE in the scintillator,

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Assessment of different ti-rads systems using computer-aided detection technology

2019 ◽  
Author(s):  
Kuen-Yuan Chen ◽  
Ming-Hsun Wu ◽  
Chiung-Nien Chen ◽  
Argon Chen
Keyword(s):  
Computer Aided Detection ◽  
Detection Technology ◽  
Computer Aided

Study on Defect Detection Technology for Bridge Deck Pavements

2017 ◽  
Vol 19 (6) ◽  
pp. 38
Author(s):  
Chengchao Guo ◽  
Pengfei Xu ◽  
Can Cui
Keyword(s):  
Defect Detection ◽  
Bridge Deck ◽  
Detection Technology
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