scholarly journals An Improved Vehicle Detection Algorithm Based on Multi-Intermediate State Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Baosen Xiao ◽  
Jingbo Xia ◽  
Xiaolu Li ◽  
Qinquan Gao
Keyword(s):  
Wireless Communication ◽  
Intermediate State ◽  
Detection System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
State Machine ◽  
Detection Accuracy ◽  
Threshold Algorithm ◽  
Magnetoresistive Sensors ◽  
Vehicle Information

The vehicle detection algorithm is an important part of the intelligent transportation system. The accuracy of the algorithm will determine whether accurate vehicle information can be obtained. The system contains several functional modules, including signal amplification, wireless communication, A/D converter, and sensor set/reset functions. To detect all the intersection vehicles, a number of magnetoresistive sensors are connected to the computer system through the wireless communication module, and then, the detected vehicle information will be transferred back to the master host computer. In this paper, two common vehicle detection algorithms, fixed threshold algorithm and adaptive threshold algorithm, were analyzed in the vehicle detection system with magnetoresistive sensors, simultaneously. Finally, an improved multi-intermediate state machine algorithm for vehicle detection was proposed. Using the intermediate state, this algorithm cannot only detect when the vehicle enters the detection area but also decide whether the vehicle leaves the sensor node or not. In this way, it improves the detection accuracy.

Implementation of Vehicle Detection System Using WSN

2013 ◽  
Vol 411-414 ◽  
pp. 1459-1464
Author(s):  
Yun Long Li ◽  
Chun Xin Wang ◽  
Xiao Li Zhou ◽  
Huan Juan Wang ◽  
Ya Kun Liu
Keyword(s):  
Road Traffic ◽  
Detection System ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Detection Algorithm ◽  
State Machine ◽  
Sensor Nodes ◽  
Guidance System ◽  
The Road ◽  
On The Road

Vehicle Detection System plays a basic role in the field of intelligent transportation, and is the cornerstone of constructing modern intelligent transportation system. This paper presents a new vehicle detection algorithm using WSN that called the adaptive state machine. The algorithm can adaptively update the threshold and baseline; use the state machine to achieve the aim of the accurate and efficient vehicle detection. It can be used for the detection of road traffic flow, and can be used in large parking vehicle guidance system. On the road, we have deployed 76 Sensor Nodes to evaluate the performance. We observe the accurate of the road vehicle detection rate of vehicle detection system is nearly 98%.

Vehicle Detection System Based on Magnetoresistive Sensor

2015 ◽  
Vol 743 ◽  
pp. 277-280 ◽  
Author(s):  
Hong He ◽  
C.Q. Mao ◽  
H.Y. Pan ◽  
Yu Li
Keyword(s):  
Wireless Communication ◽  
Theoretical Study ◽  
Detection System ◽  
Vehicle Detection ◽  
Sensor Nodes ◽  
Geomagnetic Disturbances ◽  
Magnetoresistive Sensor ◽  
Sensor Module ◽  
Communication Module ◽  
Vehicle Information

According to the vehicle after geomagnetic disturbances cause of this phenomenon, in-depth theoretical study of the anisotropic magnetoresistance AMR sensors, using triaxial anisotropic magnetoresistive sensor HMC5883L design a vehicle detection system. The system consists of a sensor module, wireless communication module. Sensor nodes through wireless communication module connected with the host computer, to achieve the detection of vehicle information.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Vision-Based Lane Line Detection System

2021 ◽  
Vol 09 (06) ◽  
Author(s):  
Poorna Vishwanth ◽  
Keyword(s):  
Automobile Industry ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Line Detection ◽  
Vertical Edge ◽  
Horizontal Edge ◽  
Driver Assistance

Since the 1990s, the rising key issue of the automobile industry is self-driving or driverless vehicles. Apparently, one of the most important challenges for smart self-driving cars comprises lane-detecting and lane-tracking capability to ensure safety and also decrease vehicle accidents for driver assistance systems. Since road lane detection is one of the most challenging tasks, driverless vehicles must learn to observe the road from a visual perspective in order to achieve automatic driving. Most of the research Works done so far can only detect the lanes or vehicles separately. However, in this paper, we propose a method to combine lane information and vehicle/obstacle information that can support the driver assistance system, driver warning system or the lane change assistant system so that it enhances the quality of results. For the lane changing system, the system detects or tracks the lane lines and detects the vehicles on all sides of a test vehicle. In lane detection, line detection algorithms such as the Canny Edge detection algorithm are used to detect the lane edges. Kalman filter will be used to track the vehicle detected from the vehicle detection algorithm. For vehicle detection, we use Otsu’s thresholding, horizontal edge filtering and vertical edge. The vertical edge filter and the Otsu’s thresholding are used to detect the vehicles on all sides of the test vehicles, then the horizontal edge is used to verify the vehicles detected.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

Realtime On-Road Vehicle Detection Approach Based on Cascaded Structure

2011 ◽  
Vol 130-134 ◽  
pp. 2429-2432
Author(s):  
Liang Xiu Zhang ◽  
Xu Yun Qiu ◽  
Zhu Lin Zhang ◽  
Yu Lin Wang
Keyword(s):  
Warning System ◽  
Vehicle Detection ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Assistance ◽  
Road Vehicle ◽  
Collision Warning ◽  
Detection Approach ◽  
Transportation Applications

Realtime on-road vehicle detection is a key technology in many transportation applications, such as driver assistance, autonomous driving and active safety. A vehicle detection algorithm based on cascaded structure is introduced. Haar-like features are used to built model in this application, and GAB algorithm is chosen to train the strong classifiers. Then, the real-time on-road vehicle classifier based on cascaded structure is constructed by combining the strong classifiers. Experimental results show that the cascaded classifier is excellent in both detection accuracy and computational efficiency, which ensures its application to collision warning system.

scholarly journals An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1358
Author(s):  
Yan Liu ◽  
Jingwen Wang ◽  
Tiantian Qiu ◽  
Wenting Qi
Keyword(s):  
High Speed ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Input Image ◽  
Research Field ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Intelligent Traffic System ◽  
Blurred Image ◽  
Blurred Images

Vehicle detection is an essential part of an intelligent traffic system, which is an important research field in drone application. Because unmanned aerial vehicles (UAVs) are rarely configured with stable camera platforms, aerial images are easily blurred. There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process. To improve the detection performance of blurred images, an end-to-end adaptive vehicle detection algorithm (DCNet) for drones is proposed in this article. First, the clarity evaluation module is used to determine adaptively whether the input image is a blurred image using improved information entropy. An improved GAN called Drone-GAN is proposed to enhance the vehicle features of blurred images. Extensive experiments were performed, the results of which show that the proposed method can detect both blurred and clear images well in poor environments (complex illumination and occlusion). The detector proposed achieves larger gains compared with SOTA detectors. The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance to shake.

scholarly journals Low-Cost Multisensor Integrated System for Online Walking Gait Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingyun Yan ◽  
Guowu Wei ◽  
Zheqi Hu ◽  
Haohua Xiu ◽  
Yuyang Wei ◽  
...  
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Integrated System ◽  
Heel Strike ◽  
Detection Accuracy ◽  
Time Error ◽  
Kinematic Data ◽  
Compact Size

A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.

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.

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