Detection Model
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2022 ◽  
Vol 70 (3) ◽  
pp. 5871-5887
Mesfer Al Duhayyim ◽  
Haya Mesfer Alshahrani ◽  
Fahd N. Al-Wesabi ◽  
Mohammed Alamgeer ◽  
Anwer Mustafa Hilal ◽  

2021 ◽  
Vol 15 (1) ◽  
pp. 194-200
Jinhwan Jang

Introduction: An automatic High-Occupancy Vehicle (HOV) lane enforcement system is developed and evaluated. Current manual enforcement practices by the police bring about safety concerns and unnecessary traffic delays. Only vehicles with more than five passengers are permitted to use HOV lanes on freeways in Korea. Hence, detecting the number of passengers in HOVs is a core element for their development. Methods: For a quick detection capability, a YOLO-based passenger detection model was built. The system comprises three infrared cameras: two are for compartment detection and the other is for number plate recognition. Multiple infrared illuminations with the same frequency as the cameras and laser sensors for vehicle detection and speed measurement are also employed. Results: The performance of the developed system is evaluated with real-world data collected on proving ground. As a result, it showed a passenger detection error of nine percent on average. The performances revealed no difference in vehicle speeds and the number of passengers according to ANOVA tests. Conclusion: Using the developed system, more efficient and safer HOV lane enforcement practices can be made.

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Li Feng ◽  
Ronghui Yan ◽  
Guangping Liu ◽  
Chen Shao

The traditional analysis method of train obstacle uses isomorphic sensors to obtain the state information and completes detection and identification analysis at the remote end of a network. A single data sample and more processing links will reduce the accuracy and speed analysis for subway encountering obstacles. To solve this problem, this paper proposes a subway obstacle perception and identification method based on cloud edge cooperation. The subway monitoring cloud platform realizes the training and construction of a detection model, and the network edge side completes the situation awareness of track state and real-time action when the train encounters obstacles. Firstly, the railroad track position is detected by cameras, and subway running track is identified by Mask RCNN algorithm to determine the detection area of obstacles in the process of subway train running. At the edge of network, the feature-level fusion of data collected by sensor cluster is carried out to provide reliable data support for detection work. Then, based on the DeepSort and YOLOv3 network models, the subway obstacle detection model is constructed on the subway monitoring cloud platform. Moreover, a trained model is distributed to the network edge side, so as to realize the fast and efficient perception and action of obstacles. Finally, the simulation verification is implemented based on actual collected datasets. Experimental results show that the proposed method has good detection accuracy and efficiency, which maintains 98.9% and 1.43 s for obstacle detection accuracy and recognition time in complex scenes.

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257884
Lei Rigi Baltazar ◽  
Mojhune Gabriel Manzanillo ◽  
Joverlyn Gaudillo ◽  
Ethel Dominique Viray ◽  
Mario Domingo ◽  

Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.

Song-tong Han ◽  
Bo Zhang ◽  
Xiao-li Rong ◽  
Lei-xiang Bian ◽  
Guo-kai Zhang ◽  

The ellipsoidal magnetization model has a wide range of application scenarios. For example, in aviation magnetic field prospecting, mineral prospecting, seabed prospecting, and UXO (unexploded ordnance) detection. However, because the existing ellipsoid magnetization formula is relatively complicated, the detection model is usually replaced by a dipole. Such a model increases the error probability and poses a significant challenge for subsequent imaging and pattern recognition. Based on the distribution of ellipsoid gravity potential and magnetic potential, the magnetic anomaly field distribution equation generated by the ellipsoid is deduced by changing the aspect ratio, making the ellipsoid equivalent to a sphere. The result of formula derivation shows that the two magnetic anomaly fields are consistent. This paper uses COMSOL finite element software to model UXO, ellipsoids, and spheres and analyzes magnetic anomalies. The conclusion shows that the ellipsoid model can completely replace the UXO model when the error range of 1nT is satisfied. Finally, we established two sets of ellipsoids and calculated the magnetic anomalous field distributions on different planes using deduction formulas and finite element software. We compared the experimental results and found that the relative error of the two sets of data was within [Formula: see text]‰. Error analysis found that the error distribution is standardized and conforms to the normal distribution. The above mathematical analysis and finite element simulation prove that the calculation method is simple and reliable and provides a magnetic field distribution equation for subsequent UXO inversion.

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6805
Jinhwan Jeon ◽  
Yoonjin Hwang ◽  
Yongseop Jeong ◽  
Sangdon Park ◽  
In So Kweon ◽  

With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented.

2021 ◽  
pp. 016555152110474
Chun Chieh Chen ◽  
Hei-Chia Wang

Online news outlets have the power to influence public policy issues. To understand the opinions of the people, many government departments check online news outlets to manually detect events that interest people. This process is time-consuming. To promptly respond to public expectations, this research proposes a framework for detecting news events that may interest government departments. This article proposes a method for finding event trigger words used to represent an event. The news media can be a critical participant in ‘agenda-setting’, which means that more widely discussed news is more attractive and critical than news that is less discussed. However, few studies have considered the influence of news media publishers from the ‘agenda setting’ perspective. Therefore, this study proposes an ‘agenda setting’-based filter to establish a high-impact news event detection model. The proposed framework identifies trigger words and utilises word embedding to find news event–related words. After that, an event detection model is designed to determine the events that are attractive to government departments. The experimental results show that purity increases from 0.666 when no extraction method is used to 0.809 when the extraction method in this study is used. The overall improvement trend shows significant improvement in event detection performance.

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 998
Linsheng Huang ◽  
Kang Wu ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Huiqin Ma ◽  

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

Hao Chen ◽  
Qi Han ◽  
Qiong Li ◽  
Xiaojun Tong

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Han Yuan ◽  
Sen Liu ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

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