scholarly journals A Novel Vehicle Detection Method Based on the Fusion of Radio Received Signal Strength and Geomagnetism

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 58 ◽  
Author(s):  
Liangliang Lou ◽  
Jinyi Zhang ◽  
Yong Xiong ◽  
Yanliang Jin
Keyword(s):  
Error Detection ◽  
Detection Method ◽  
Signal Strength ◽  
Vehicle Detection ◽  
Received Signal Strength ◽  
Detection Accuracy ◽  
Detection Problem ◽  
Traffic Light ◽  
Radio Stations ◽  
Multiple Detection

A geomagnetic signal blind zone exists between the front and rear axle of high-chassis vehicle such as trucks and buses, which leads to multiple-detection problem in detecting those vehicles running at low speed on roads or error-detection problem in the case of the stopping position of the vehicle is not standard when waiting for the traffic light to change. In order to improve the detection accuracy of any type of vehicle running at any speed, a novel two-sensors data fusion vehicle detection method through combining received signal strength from radio stations with geomagnetism around vehicles is designed and verified in the paper. Experimental results show that the accuracy of our proposed method can reach 95.4% and traditional single magnetism-based detection method was only 83.4% in the detection of high-chassis vehicles.

scholarly journals Bacterial colonies detecting and counting based on enhanced CNN detection method

2021 ◽  
Vol 233 ◽  
pp. 02012
Author(s):  
Shousheng Liu ◽  
Zhigang Gai ◽  
Xu Chai ◽  
Fengxiang Guo ◽  
Mei Zhang ◽  
...  
Keyword(s):  
Target Detection ◽  
Error Detection ◽  
Detection Rate ◽  
Detection Method ◽  
Difficult Problem ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Model ◽  
Confidence Threshold ◽  
Small Targets

Bacterial colonies detecting and counting is tedious and time-consuming work. Fortunately CNN (convolutional neural network) detection methods are effective for target detection. The bacterial colonies are a kind of small targets, which have been a difficult problem in the field of target detection technology. This paper proposes a small target enhancement detection method based on double CNNs, which can not only improve the detection accuracy, but also maintain the detection speed similar to the general detection model. The detection method uses double CNNs. The first CNN uses SSD_MOBILENET_V1 network with both target positioning and target recognition functions. The candidate targets are screened out with a low confidence threshold, which can ensure no missing detection of small targets. The second CNN obtains candidate target regions according to the first round of detection, intercepts image sub-blocks one by one, uses the MOBILENET_V1 network to filter out targets with a higher confidence threshold, which can ensure good detection of small targets. Through the two-round enhancement detection method has been transplanted to the embedded platform NVIDIA Jetson AGX Xavier, the detection accuracy of small targets is significantly improved, and the target error detection rate and missed detection rate are reduced to less than 1%.

scholarly journals An Improved Roadside Parking Space Occupancy Detection Method Based on Magnetic Sensors and Wireless Signal Strength

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2348 ◽  
Author(s):  
Liangliang Lou ◽  
Jinyi Zhang ◽  
Yong Xiong ◽  
Yanliang Jin
Keyword(s):  
Detection Method ◽  
Dead Zone ◽  
Sampling Rate ◽  
Vehicle Detection ◽  
Magnetic Sensors ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Parking Space ◽  
Wireless Transceivers ◽  
Occupancy Detection

Smart Parking Management Systems (SPMSs) have become a research hotspot in recent years. Many researchers are focused on vehicle detection technology for SPMS which is based on magnetic sensors. Magnetism-based wireless vehicle detectors (WVDs) integrate low-power wireless communication technology, which improves the convenience of construction and maintenance. However, the magnetic signals are not only susceptible to the adjacent vehicles, but also affected by the magnetic signal dead zone of high-chassis vehicles, resulting in a decrease in vehicle detection accuracy. In order to improve the vehicle detection accuracy of the magnetism-based WVDs, the paper introduces an RF-based vehicle detection method based on the characteristics analysis of received signal strengths (RSSs) generated by the wireless transceivers. Since wireless transceivers consume more energy than magnetic sensors, the proposed RF-based method is only activated to extract the data characteristics of RSSs to further judge the states of vehicles when the data feature of magnetic signals is not sufficient to provide accurate judgment on parking space status. The proposed method was evaluated in an actual roadside parking lot and experimental results show that when the sampling rate of magnetic sensor is 1 Hz, the vehicle detection accuracy is up to 99.62%. Moreover, compared with machine-learning-based vehicle detection method, the experimental results show that our method has achieved a good compromise between detection accuracy and power consumption.

Video Vehicle Detection Method Based on Multiple Color Space Information Fusion

2012 ◽  
Vol 546-547 ◽  
pp. 721-726
Author(s):  
Hong Xiang Shao ◽  
Xiao Ming Duan
Keyword(s):  
Information Fusion ◽  
Traditional Method ◽  
Detection Method ◽  
Color Space ◽  
Vehicle Detection ◽  
Data Accuracy ◽  
Traffic Information ◽  
Detection Accuracy ◽  
Relative Independence ◽  
Hsi Color Space

A detection method which selective fuses the nine detection results of RGB, YCbCr and HSI color space according to the image color space relative independence of each component and complementarities is approached in order to improve vehicle video detection accuracy. The method fuses three different detection results in nine components by the value of H when the value of both S and I are higher and does another three detection results when the value of both S and I are smaller. Experiments show that the method compared to the traditional method using only the detection results of the brightness component improved substantial, reduced empty of the detected vehicle a large extent and increased traffic information data accuracy depending on the detection result.

scholarly journals System design for passive human detection using principal components of the signal strength space

2013 ◽  
Vol 10 (1) ◽  
pp. 423-452 ◽  
Author(s):  
Bojan Mrazovac ◽  
Milan Bjelica ◽  
Dragan Kukolj ◽  
Branislav Todorovic ◽  
Sasa Vukosavljev
Keyword(s):  
Principal Components ◽  
Detection Method ◽  
Power Saving ◽  
Signal Strength ◽  
Human Detection ◽  
Detection Accuracy ◽  
Human Presence ◽  
Development Costs ◽  
Human Presence Detection ◽  
Radio Signals

In this article, device-free human presence detection method based on principal components analysis of the radio signal strength variations is proposed. The method increases user awareness for automated power management interaction in residential power saving systems. Motivation comes from a need for decreasing the installation complexity and development costs induced by the integration of specific human presence detection sensors. The method exploits the fact that a human body interferes with radio signals by introducing irregularities in the radio signature which indicate possible human presence. By analyzing radio signals between radio transceivers embedded in 2.4 GHz wireless power outlets, the original environment is not visually modified and a certain level of sensorial intelligence is introduced without additional sensors. The analysis of the signal strength variations in principal components space enhances the detection accuracy level of human presence detection method, retaining low installation costs and improving overall energy conservation.

A Fast Forward Vehicle Detection Method by Multiple Vision Clues Fusion

2014 ◽  
Vol 543-547 ◽  
pp. 2647-2651
Author(s):  
Tai Qi Wu ◽  
Ye Zhang ◽  
Bin Bin Wang ◽  
Jia Heng Yu ◽  
De Wei Zhu
Keyword(s):  
Detection Method ◽  
System Development ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Vehicle Body ◽  
Detection Accuracy ◽  
Collision Warning ◽  
Moving Vehicle ◽  
Forward Collision Warning

With the development of intelligent vehicle technology, vehicle detection based on vision analysis has become an research hotspot in forward collision warning system development. Aiming to solve the existing problems in the current vehicle detection methods, for example, the detection accuracy is sensitive to the variation of illumination and object angle, we propose a forward moving vehicle detection method according to multiple vision clues fusion. Firstly, we locate the rough position using vehicle bottom shadow detection. The shadow is detected using an adaptive threshold image segmentation approach twice. Secondly, the symmetry of vehicle body and the perspective of camera field of view are both referenced to remove the inaccurate location in the first stage. The proposed method has been tested on several videos recorded in real urban conditions. Experimental results show that our method achieves 93.67% average detection accuracy in daytime, and its processing speed is more than 25fps. The proposed method has certain application prospects for improving the vision based forward collision warning system performance.

A Detection System of Impurity in Transparent Liquid

2014 ◽  
Vol 1003 ◽  
pp. 193-197 ◽  
Author(s):  
Bin Huang ◽  
Ping Wang ◽  
Si Le Ma
Keyword(s):  
Error Detection ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Difficult Problem ◽  
Flow Diagram ◽  
Detection Accuracy ◽  
Liquid Impurity ◽  
Transparent Liquid ◽  
Impurity Detection

In the fields of transparent liquid impurity detection based on machine vision technology, how to effectively detect impurities in the liquid is a difficult problem which has not yet been solved, mainly in the low recognition rate, the slow recognition speed, and the phenomenon of error detection and undetected. Therefore, this paper presents a new impurity detection method. Firstly, the hardware structure of the system is introduced in this paper. Then the flow diagram of impurity detection is presented. Finally, the algorithm of impurity detection is studied. Experiments show that the system introduced in this paper can identify impurities in liquid well on condition of ensuring the detection speed and detection accuracy.

scholarly journals Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2012
Author(s):  
JongBae Kim
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Detection Method ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Noise Removal ◽  
Detection Accuracy ◽  
Vehicle Exhaust ◽  
Learning Techniques ◽  
Real Time Detection

This paper proposes a real-time detection method for a car driving ahead in real time on a tunnel road. Unlike the general road environment, the tunnel environment is irregular and has significantly lower illumination, including tunnel lighting and light reflected from driving vehicles. The environmental restrictions are large owing to pollution by vehicle exhaust gas. In the proposed method, a real-time detection method is used for vehicles in tunnel images learned in advance using deep learning techniques. To detect the vehicle region in the tunnel environment, brightness smoothing and noise removal processes are carried out. The vehicle region is learned after generating a learning image using the ground-truth method. The YOLO v2 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments. The vehicle detection rate is approximately 87%, while the detection accuracy is approximately 94% for the proposed method applied to various tunnel road environments.

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