IBMDA: Information based misbehavior detection algorithm for VANET

2020 ◽  
Vol 26 (3) ◽  
pp. 185-207
Author(s):  
Dinesh Singh ◽  
Ranvijay ◽  
Rama Shankar Yadav
Keyword(s):  
Information Sharing ◽  
High Speed ◽  
Cluster Head ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Misbehavior Detection ◽  
Incident Delay ◽  
Event Information ◽  
Safety Event ◽  
On The Road

The safety event information sharing among the vehicles in motion is the primary goal to design the vehicular ad hoc network (VANET). The shared safety event information assists vehicles to avoid road accidents and driving inconvenience. The advantages of safety event information sharing in VANET has become blunt due to the misbehavior of vehicles. The vehicle’s misbehavior like dissemination of false information, reply of bogus messages, etc., can create traffic hazards on the road and may result in the loss of property and human lives. In VANET, the detection of such misbehaving vehicles along with minimum time delay in flooding safety event information (i.e., incident delay) to others is challenging due to the high speed of vehicles. The formation of stable VANET topology is a feasible solution among many to improve the performance of misbehavior detection and reducing incident delay even with high speed of vehicles. In this paper, we propose an information based misbehavior detection algorithm (IBMDA) that effectively works in stable cluster based VANET. Our proposed IBMDA algorithm that runs on the selected cluster head vehicles is used to verify the content of received safety event messages. The identification of vehicles as malicious or non malicious depends on the result of verification at cluster heads. An illustrative example is given to explore our proposed algorithm easily and effectively. The highway scenario is considered to test the performance of our proposed IBMDA algorithm. The simulation is performed with a detailed comparative analysis using ns-3 simulator. It is observed that under the considered scenario, our proposed algorithm improves the misbehavior detection accuracy up to 6.46% and reduces average incident delay approximately up to 14.78% as compared to existing algorithms.

scholarly journals A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Wang ◽  
Ziyue Wang ◽  
Zhengwei Xu ◽  
Chi Wang ◽  
Qiang Li ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Speed ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Time Operation ◽  
On The Road

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

scholarly journals RCVC: RSU-Aided Cluster-Based Vehicular Clouds Architecture for Urban Areas

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 193
Author(s):  
Mohamed Ben bezziane ◽  
Ahmed Korichi ◽  
Chaker Abdelaziz Kerrache ◽  
Mohamed el Amine Fekair
Keyword(s):  
Urban Areas ◽  
High Speed ◽  
Service Discovery ◽  
Cluster Head ◽  
Experimental Comparison ◽  
The Road ◽  
Road Side Unit ◽  
End To End Delay ◽  
End To End ◽  
On The Road

As a promising topic of research, Vehicular Cloud (VC) incorporates cloud computing and ad-hoc vehicular network (VANET). In VC, supplier vehicles provide their services to consumer vehicles in real-time. These services have a significant impact on the applications of internet access, storage and data. Due to the high-speed mobility of vehicles, users in consumer vehicles need a mechanism to discover services in their vicinity. Besides this, quality of service varies from one supplier vehicle to another; thus, consumer vehicles attempt to pick out the most appropriate services. In this paper, we propose a novel protocol named RSU-aided Cluster-based Vehicular Clouds protocol (RCVC), which constructs the VC using the Road Side Unit (RSU) directory and Cluster Head (CH) directory to make the resources of supplier vehicles more visible. While clusters of vehicles that move on the same road form a mobile cloud, the remaining vehicles form a different cloud on the road side unit. Furthermore, the consumption operation is achieved via the service selection method, which is managed by the CHs and RSUs based on a mathematical model to select the best services. Simulation results prove the effectiveness of our protocol in terms of service discovery and end-to-end delay, where we achieved service discovery and end-to-end delay of 3 × 10−3 s and 13 × 10−2 s, respectively. Moreover, we carried out an experimental comparison, revealing that the proposed method outperformed several states of the art protocols.

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.

Mask Wearing Detection Algorithm Based on Improved Tiny YOLOv3

2021 ◽  
pp. 2155007
Author(s):  
Guohua Liu ◽  
Qintao Zhang
Keyword(s):  
Loss Function ◽  
Network Structure ◽  
High Speed ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Bounding Box

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on [Formula: see text]-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.

Based on the Human Eye Fatigue Detection System of Recognition and FPGA Technology

2014 ◽  
Vol 672-674 ◽  
pp. 1995-1998 ◽  
Author(s):  
Jun Wang ◽  
Qiang Liu ◽  
Hang Zhao
Keyword(s):  
High Speed ◽  
Detection System ◽  
A Priori ◽  
Color Space ◽  
Gaussian Model ◽  
Region Growing ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Threshold Detection ◽  
Human Eye

This paper introduces the field programmable gate array (fpga) application in high-speed visual inspection system.ALTERA EP1K30QC208-2 are used in the system for system calculation and control of the core, to perform high-speed real-time visual detection algorithm, this paper adopts a yawning based on eye closure and to detect driver fatigue, the method of in YCr, Cb in the color space using gaussian model skin detection of human face area, in the face of a gray binarization figure using a priori knowledge of facial features geometry in rough positioning the human eye, eye contour are obtained by region growing and morphological operation and calculation of the closure of the eye;Best threshold detection lips when using lip color roughly locate the lips, on the basis of accurate positioning lips by face grey value characteristics, and then through the mouth level to determine whether a driver yawn;Based on the two characteristics of driving fatigue, experiments show that this system detection speed, excellent versatility, and can improve the detection accuracy.

scholarly journals Accurate and High-speed FBG Demodulator Using STM32 and DOG Algorithm

2021 ◽  
Author(s):  
LU WEI JIA ◽  
Lin Yan Zhang ◽  
Liu Yi ◽  
Pan YU HENG ◽  
Li GuoYan
Keyword(s):  
Standard Deviation ◽  
Temperature Sensitivity ◽  
High Speed ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Average Error ◽  
Detection Accuracy ◽  
Peak Detection Algorithm ◽  
Difference Of Gaussian ◽  
Wavelength Detection

Abstract A multi-channel and high-speed FBG demodulator based STM32 is designed in this paper. The wavelength detection accuracy is improved using a difference of Gaussian (DoG) peak detection algorithm. The 16-channel FBG wavelengths are demodulated simultaneously and that the demodulation frequency can reach 1kHz. The experimental results show that the temperature sensitivity is 13.17 pm/ ºC. And meanwhile, the standard deviation and the average error of the FBG wavelength demodulated at the same temperature is 1.9 pm and 2.1 pm respectively.

scholarly journals AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5326
Author(s):  
Balakrishnan Ramalingam ◽  
Thein Tun ◽  
Rajesh Elara Mohan ◽  
Braulio Félix Gómez ◽  
Ruoxi Cheng ◽  
...  
Keyword(s):  
Built Environment ◽  
Information Sharing ◽  
Real World ◽  
Confidence Level ◽  
Performance Metrics ◽  
Detection Algorithm ◽  
Activity Monitoring ◽  
Detection Accuracy ◽  
Activity Detection ◽  
3D Printed

Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon." The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.

Misbehavior Detection Algorithm in CCSDS Space Telecommand Link

2010 ◽  
Vol 32 (2) ◽  
pp. 290-295
Author(s):  
Lei Zhang ◽  
Cheng-jin An ◽  
Quan Zhang ◽  
Chao-jing Tang
Keyword(s):  
Detection Algorithm ◽  
Misbehavior Detection

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

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