scholarly journals Real-Time Vision through Haze Based on Polarization Imaging

2019 ◽  
Vol 9 (1) ◽  
pp. 142 ◽  
Author(s):  
Xinhua Wang ◽  
Jihong Ouyang ◽  
Yi Wei ◽  
Fei Liu ◽  
Guang Zhang
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Detection Efficiency ◽  
Detection System ◽  
Weather Conditions ◽  
Pollutant Emission ◽  
Optical Monitoring ◽  
Stokes Vectors ◽  
Bad Weather ◽  
Pollutant Discharge

Various gases and aerosols in bad weather conditions can cause severe image degradation, which will seriously affect the detection efficiency of optical monitoring stations for high pollutant discharge systems. Thus, penetrating various gases and aerosols to sense and detect the discharge of pollutants plays an important role in the pollutant emission detection system. Against this backdrop, we recommend a real-time optical monitoring system based on the Stokes vectors through analyzing the scattering characteristics and polarization characteristics of both gases and aerosols in the atmosphere. This system is immune to the effects of various gases and aerosols on the target to be detected and achieves the purpose of real-time sensing and detection of high pollutant discharge systems under bad weather conditions. The imaging system is composed of four polarizers with different polarization directions integrated into independent cameras aligned parallel to the optical axis in order to acquire the Stokes vectors from various polarized azimuth images. Our results show that this approach achieves high-contrast and high-definition images in real time without the loss of spatial resolution in comparison with the performance of conventional imaging techniques.

scholarly journals City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather

2020 ◽  
Vol 12 (9) ◽  
pp. 1401
Author(s):  
Dong Zhao ◽  
Yuta Asano ◽  
Lin Gu ◽  
Imari Sato ◽  
Huixin Zhou
Keyword(s):  
Extinction Coefficient ◽  
Near Infrared ◽  
Imaging System ◽  
Weather Conditions ◽  
Light Extinction ◽  
Selection Strategy ◽  
Infrared Wavelengths ◽  
The Difference ◽  
Bad Weather ◽  
Almost All

In this paper, we propose a novel city-scale distance sensing algorithm based on atmosphere optics. The suspended particles, especially in bad weather, would attenuate the light at almost all wavelengths. Observing this fact and starting from the light scattering mechanism, we derive a bispectral distance sensing algorithm by leveraging the difference of extinction coefficient between two specifically selected near infrared wavelengths. The extinction coefficient of the atmosphere is related to both wavelength and meteorological conditions, also known as visibility, such as the fog and haze day. To account for different bad weather conditions, we explicitly introduce visibility into our algorithm by incorporating it into the calculation of extinction coefficient, making our algorithm simple yet effective. To capture the data, we build a bispectral imaging system that is able to take a pair of images with a monochrome camera and two narrow band-pass filters. We also present a wavelength selection strategy that allows us to accurately sense distance regardless of material reflectance and texture. Specifically, this strategy determines two distinct near infrared wavelengths by maximising the extinction coefficient difference while minimizing the influence of building’s reflectance variance. The experiments empirically validate our model and its practical performance on the distance sensing for the city-scale buildings.

scholarly journals Real Time In-situ Detection System Research for Methane in Offshore Shallow Gas based on Thin Film Interface

2018 ◽  
Vol 232 ◽  
pp. 04053
Author(s):  
Cheng-xing Miao ◽  
Qing Li ◽  
Sheng-yao Jia
Keyword(s):  
Real Time ◽  
Detection Efficiency ◽  
Detection System ◽  
Detection Methods ◽  
Shallow Gas ◽  
Micro Gas Sensor ◽  
Detection Probe ◽  
On Line ◽  
In Situ Detection

In order to get ridded of the non real-time detection methods of artificial site sampled and laboratory instrument analyzed in the field of methane detection in the offshore shallow gas, real-time in-situ detection system for methane in offshore shallow gas was designed by the film interface.The methane in the offshore shallow gas through the gas-liquid separation membrane of polymer permeation into the system internal detection probe, analog infrared micro gas sensor sensed the methane concentration and the corresponded output value, data acquisition and communication node fitted into standard gas concentration.Based on the experimental data compared with the traditional detection method, and further analyzed the causes of error produced by the case experiment. The application results show that the system can achieve a single borehole layout, long-term on-line in-situ on-line detection, and improve the detection efficiency and the timeliness of the detection data.

Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest

2019 ◽  
Vol 2673 (8) ◽  
pp. 221-232 ◽  
Author(s):  
Md Nasim Khan ◽  
Mohamed M. Ahmed
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Detection System ◽  
Weather Conditions ◽  
Video Camera ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Weather Information

Snowfall negatively affects pavement and visibility conditions, making it one of the major causes of motor vehicle crashes in winter weather. Therefore, providing drivers with real-time roadway weather information during adverse weather is crucial for safe driving. Although road weather stations can provide weather information, these stations are expensive and often do not represent real-time trajectory-level weather information. The main motivation of this study was to develop an affordable in-vehicle snow detection system which can provide trajectory-level weather information in real time. The system utilized SHRP2 Naturalistic Driving Study video data and was based on machine learning techniques. To train the snow detection models, two texture-based image features including gray level co-occurrence matrix (GLCM) and local binary pattern (LBP), and three classification algorithms: support vector machine (SVM), k-nearest neighbor (K-NN), and random forest (RF) were used. The analysis was done on an image dataset consisting of three weather conditions: clear, light snow, and heavy snow. While the highest overall prediction accuracy of the models based on the GLCM features was found to be around 86%, the models considering the LBP based features provided a much higher prediction accuracy of 96%. The snow detection system proposed in this study is cost effective, does not require a lot of technical support, and only needs a single video camera. With the advances in smartphone cameras, simple mobile apps with proper data connectivity can effectively be used to detect roadway weather conditions in real time with reasonable accuracy.

Fully Automated and Wirelessly Enabled Drillstem Tests: Seven-Zones Campaign Case Study in Sakhalin

2021 ◽  
Author(s):  
Elias Temer ◽  
Deiveindran Subramaniam
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Time Window ◽  
Weather Conditions ◽  
Testing Time ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Operations Control ◽  
Bad Weather

Abstract Well test is one of the crucial steps required to forecast production investments of their fields. However, the operators face many challenges such as reduced capex, exploration budgets, and bad weather conditions that limit the well testing time window. To overcome these challenges, an automated well testing platform enabled a real time monitoring and controlling more zones in a single run for appraisal wells in the Sea of Okhotsk, Russia. This article highlights the test objectives, the job planning, and automated execution of wirelessly enabled operations in very hostile conditions and limited time period. The use of a telemetry system to well test seven zones allowed real-time data acquisition, control of critical downhole equipment, data transmission to the operator's office in town. Various operational cases will be discussed to demonstrate how automated data acquisition and downhole operations control has optimized operations for both the service company and the operator.

ASSESSMENT OF TARGET DETECTION ABILITY FOR LASER FUZES IN FOG CONDITIONS

2021 ◽  
pp. 8-16
Author(s):  
Linh
Keyword(s):  
Target Detection ◽  
Detection Probability ◽  
Detection Efficiency ◽  
Signal To Noise Ratio ◽  
Evaluation Model ◽  
Laser System ◽  
Weather Conditions ◽  
Signal To Noise ◽  
Detection Ability ◽  
Bad Weather

The article presents a method to evaluate the target detection efficiency of laser fuzes operating in foggy conditions. The evaluation model is built from: the distance equation of the laser system, the attenuation of the beam in two-way propagation, the disturbances affecting the system; the signal to noise ratio SRN has determined the detection probability of the receiver. The model was used to evaluate with wavelengths: 850 nm, 1000 nm and 1550 nm, when propagating in three different bad weather conditions. The results show that the most effective detection of the target when using a wavelength of 1550 nm in visibility in haze and mist conditions (visibility V > 500 m). In fog conditions (visibility V < 500 m), the above three wavelengths provide the same detection efficiency. The article provides the method and instructions for choosing the wavelength of the laser fuze.

Defects Detection of Printed Circuit Board Based on the Machine Vision Method

2014 ◽  
Vol 494-495 ◽  
pp. 785-788 ◽  
Author(s):  
Wen Bin Wang ◽  
Dao Yuan Liu ◽  
Yu Qin Yao
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Real Time ◽  
Printed Circuit Board ◽  
Detection Efficiency ◽  
Detection System ◽  
Circuit Board ◽  
Algorithm Analysis ◽  
Real Time Processing ◽  
Morphological Image Processing

The processing of target image using image processing technology, can realize the non-contact online detecting circuit board, thus greatly improve the detection efficiency, reducing the defective rate. This paper provides the detection system based on the methods of pre-processing the standard circuit board image and the circuit board image to be detected, two value segmentation, morphological image processing, image registration and poor shadow detection processing, among them ,image registration is the key. In order to improve the processing speed to achieve real-time processing, image registration using rapid processing algorithm. Analysis of the experimental results, the method can detect the defects on the circuit board to be detected accurately, and can achieve the automatic real-time detection purposes.

scholarly journals Micro pin header defect detection system based on OpenCV

2021 ◽  
Vol 2137 (1) ◽  
pp. 012037
Author(s):  
Houcheng Yang ◽  
Yinxin Yan ◽  
Zhangsi Yu ◽  
Zhang Ning
Keyword(s):  
Image Processing ◽  
Success Rate ◽  
Real Time ◽  
Light Source ◽  
Defect Detection ◽  
Detection Efficiency ◽  
Detection System ◽  
Experimental Tests ◽  
Quality Inspection ◽  
Detection Error

Abstract In order to solve the problems of low detection efficiency and large detection error in the process of manual quality inspection, a full-automatic defect detection system is built. The system uses an industrial camera, selects a suitable light source for image acquisition, uses the open source OpenCV visual library for image processing and defect contour recognition, and sets the screening conditions for unqualified products. The system can detect whether the needle arrangement has defects in real time and classify them according to different defect categories, It can greatly improve the detection efficiency of needle arranging production enterprises. Through a large number of experimental tests, the detection success rate can reach 98.67%, which shows that the system is feasible.

scholarly journals Driver Preference and Level of Trust Related to Real-Time Road Freezing Risk Information

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Moataz Bellah Ben Khedher ◽  
Choong Heon Yang ◽  
Jin Guk Kim ◽  
Duk Geun Yun ◽  
Sung Pil Shin
Keyword(s):  
Real Time ◽  
Information Service ◽  
Winter Season ◽  
Surface Friction ◽  
Weather Conditions ◽  
Risk Information ◽  
Navigation Systems ◽  
Adverse Weather ◽  
Driver's License ◽  
Bad Weather

Bad weather conditions can affect normal driving by substantially hindering visibility. Among all adverse weather conditions, road freezing is probably the most dangerous to drivers because slippery roads reduce surface friction and can lead to loss of vehicle control. This paper evaluates driver preference of receiving real-time road freezing risk information and explores the factors that would most influence drivers’ trust in a future road freezing information service. A survey was conducted in the metropolitan areas of South Korea during January and February 2019. The survey was completed by 231 driver’s license holders of 18 years or older, and the results were used for statistical analysis. According to the survey results, the variable message sign (VMS) is a very important system from the perspective of public benefit. Car-navigation systems are preferred for age categories of 21∼30 and over 50. In addition, ordinal regression was used to analyze the causal relationship between the level of trust regarding road freezing risk information and its controlling factors. The ordered log odds of drivers with previous accident experience due to slippery roads exhibit a higher level of trust in road freezing risk information because the coefficient is positive. Moreover, drivers with a constant commute time show a lower level of trust in road freezing risk information. These findings provide a foundation for planning the scope of future road freezing risk information service, as well as the specific service targets and type of information, especially during the winter season.

scholarly journals A Real-Time Vehicle Detection System under Various Bad Weather Conditions Based on a Deep Learning Model without Retraining

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5731 ◽  
Author(s):  
Xiu-Zhi Chen ◽  
Chieh-Min Chang ◽  
Chao-Wei Yu ◽  
Yen-Lin Chen
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Vehicle Detection ◽  
Weather Conditions ◽  
Learning Model ◽  
Detection Methods ◽  
Computational Time ◽  
Detection Accuracy ◽  
Bad Weather ◽  
Deep Learning Model

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder–decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.

scholarly journals An Analysis on the Performance of a Mobile Platform with Gas Sensors for Real Time Victim Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2018
Author(s):  
Antonios Anyfantis ◽  
Spyridon Blionas
Keyword(s):  
Real Time ◽  
Gas Sensors ◽  
Detection System ◽  
Weather Conditions ◽  
Urban Environments ◽  
Mobile Platform ◽  
Building Structures ◽  
Local Weather ◽  
Survival Space ◽  
Sources Of Co2

This work concerns the performance analysis of the sensors contained in a victim detection system. The system is a mobile platform with gas sensors utilized for real time victim localization in urban environments after a disaster has caused the entrapment of people in partially collapsed building structures. The operating principle of the platform is the sampling of air from potential survival spaces (voids) and the measurement of the sampled air’s temperature and concentration of CO2 and O2. Humans in a survival space are modelled as sources of CO2 and heat and sinks of O2. The physical openings of a survival space are modelled as sources of fresh air and sinks of the internal air. These sources and sinks dynamically affect the monitored properties of the air inside a survival space. In this paper, the effects of fresh air sources and internal air sinks are first examined in relation to local weather conditions. Then, the effect of human sources of CO2 and sinks of O2 in the space are examined. A model is formulated in order to reliably estimate the concentration of CO2 and O2 as a function of time for given reasonable entrapment scenarios. The input parameters are the local weather conditions, the openings of the survival space, and the number and type of entrapped humans. Three different tests successfully verified the presented theoretical estimations. A detection system with gas sensors of specified or measured capabilities, by utilizing this model and based on the expected concentrations, may inform the operator of the minimum required presence of humans in a survival space that can be detected after “some time”.

Sign in / Sign up

Export Citation Format

Share Document