A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.

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Design and Development of Street Crack Detection

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.8.15226 ◽

2018 ◽

Vol 7 (3.8) ◽

pp. 82 ◽

Cited By ~ 1

Author(s):

Mr Swapnil Vilas Patil ◽

Prof. Mangesh M. Ghonge ◽

. .

Keyword(s):

Image Processing ◽

Crack Detection ◽

Ground Truth ◽

Automated Detection ◽

Driving Safety ◽

Detection Accuracy ◽

False Detection ◽

Safety Assurance ◽

Ground Truth Image ◽

Better Than

Automated detection of street cracks is a crucial project. In transportation preservation for driving safety assurance and detection a crack manually is an exceptionally tangled and time excessive method. So with the advance of science and generation, automated structures with intelligence have been accustomed examine cracks instead of people. For crack detection and characterization image processing is used widely. But because of the inhomogeneity along the cracks, the inference of noise with the same texture and complexity of cracks, image processing remain challenging. In this paper, we focused on the system performance and the additional features. System which has crack detection accuracy issue, false detection of crack issue, efficiency issue are solved in this system. For better accuracy in detecting crack and increasing the performance of the system we used the random forest algorithm. This system help to detect and characterized the crack and it find out crack from noise also i.e. it neglect the noise better than existing system. Similarly, proposed method find out the length of the crack width and depth of the crack from image with the help of ground truth image.

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Cymbal Piezoelectric Composite Transducer for Soccer Robot Attitude Detection Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.2851 ◽

2014 ◽

Vol 513-517 ◽

pp. 2851-2854

Author(s):

Xiao Fei Xu

Keyword(s):

Vibration Mode ◽

Detection System ◽

Piezoelectric Composite ◽

Detection Accuracy ◽

Soccer Robot ◽

Support Points ◽

The Stability ◽

Attitude Detection ◽

Processing Circuit ◽

Better Than

This paper proposed a soccer robots attitude detection method based on the principle and vibration mode of cymbal piezoelectric composite transducer. Cymbal piezoelectric composite transducers and MEMS-based three-axis low-g sensor were set on some different support points of soccer robot's joint to accomplish attitude detection. The signal processing circuit and algorithm software were designed. The research results showed that the performance parameters of the soccer robot falling down attitude detection system based on cymbal piezoelectric composite transducer are better than the other sensor, such as the sensitivity, correct, undetected fall and stability duration, etc. The experiment results showed that the detection accuracy for falling down of the soccer robot can be 98% and the stability duration can be 60 minutes.

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Design and Implementation of Automated Steganography Image-Detection System for the KakaoTalk Instant Messenger

Computers ◽

10.3390/computers9040103 ◽

2020 ◽

Vol 9 (4) ◽

pp. 103

Author(s):

Jun Park ◽

Youngho Cho

Keyword(s):

Detection System ◽

Automated Detection ◽

Automated System ◽

Detection Methods ◽

Image Detection ◽

Social Network Service ◽

Detection Algorithms ◽

Instant Messenger ◽

And Control ◽

Better Than

As the popularity of social network service (SNS) messengers (such as Telegram, WeChat or KakaoTalk) grows rapidly, cyberattackers and cybercriminals start targeting them, and from various media, we can see numerous cyber incidents that have occurred in the SNS messenger platforms. Especially, according to existing studies, a novel type of botnet, which is the so-called steganography-based botnet (stego-botnet), can be constructed and implemented in SNS chat messengers. In the stego-botnet, by using various steganography techniques, every botnet communication and control (C&C) messages are secretly embedded into multimedia files (such as image or video files) frequently shared in the SNS messenger. As a result, the stego-botnet can hide its malicious messages between a bot master and bots much better than existing botnets by avoiding traditional botnet-detection methods without steganography-detection functions. Meanwhile, existing studies have focused on devising and improving steganography-detection algorithms but no studies conducted automated steganography image-detection system although there are a large amount of SNS chatrooms on the Internet and thus may exist many potential steganography images on those chatrooms which need to be inspected for security. Consequently, in this paper, we propose an automated system that detects steganography image files by collecting and inspecting all image files shared in an SNS chatroom based on open image steganography tools. In addition, we implement our proposed system based on two open steganography tools (Stegano and Cryptosteganography) in the KakaoTalk SNS messenger and show our experimental results that validate our proposed automated detection system work successfully according to our design purposes.

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Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽

10.3390/s20133635 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3635 ◽

Cited By ~ 1

Author(s):

Guoming Zhang ◽

Xiaoyu Ji ◽

Yanjie Li ◽

Wenyuan Xu

Keyword(s):

Smart Grid ◽

Power Consumption ◽

Real Time ◽

Condition Monitoring ◽

Resource Constraints ◽

Detection System ◽

Stable Operation ◽

Detection Accuracy ◽

Monitoring Method ◽

Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

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Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device

Sensors ◽

10.3390/s21103419 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3419

Author(s):

Shan Zhang ◽

Zihan Yan ◽

Shardul Sapkota ◽

Shengdong Zhao ◽

Wei Tsang Ooi

Keyword(s):

Ground Truth ◽

Detection Accuracy ◽

Continuous Performance Task ◽

Eeg Signals ◽

Gradual Onset ◽

Machine Learning Approach ◽

A New Technique ◽

Thought Probes ◽

Labeling Method ◽

Fluctuation Measurement

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

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Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

Applied Sciences ◽

10.3390/app11114894 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4894

Author(s):

Anna Scius-Bertrand ◽

Michael Jungo ◽

Beat Wolf ◽

Andreas Fischer ◽

Marc Bui

Keyword(s):

Object Detection ◽

State Of The Art ◽

Positive Impact ◽

Detection System ◽

Training Data ◽

Detection Accuracy ◽

Current State ◽

Alignment Task ◽

Scanned Image ◽

Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

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Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

ACM Transactions on Internet Technology ◽

10.1145/3448614 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1-22

Author(s):

Celestine Iwendi ◽

Saif Ur Rehman ◽

Abdul Rehman Javed ◽

Suleman Khan ◽

Gautam Srivastava

Keyword(s):

Internet Of Things ◽

Detection System ◽

Experimental Testing ◽

Cyber Attacks ◽

Internet Technology ◽

The Internet ◽

Business Operations ◽

Private Data ◽

The Internet Of Things ◽

Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

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Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽

10.3390/s21051820 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1820

Author(s):

Xiaotao Shao ◽

Qing Wang ◽

Wei Yang ◽

Yun Chen ◽

Yi Xie ◽

...

Keyword(s):

Semantic Information ◽

Detection System ◽

Pedestrian Detection ◽

Detection Accuracy ◽

The Public ◽

Scale Feature ◽

Detection Algorithms ◽

Multi Scale ◽

Art Works ◽

Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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Anomaly Detection Using Deep Neural Network for IoT Architecture

Applied Sciences ◽

10.3390/app11157050 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7050

Author(s):

Zeeshan Ahmad ◽

Adnan Shahid Khan ◽

Kashif Nisar ◽

Iram Haider ◽

Rosilah Hassan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Anomaly Detection ◽

Deep Neural Network ◽

Detection System ◽

Traffic Monitoring ◽

Detection Accuracy ◽

Learning Models ◽

Prime Concern ◽

Entry Points

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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

Sensors ◽

10.3390/s21041081 ◽

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.

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