Intelligent masked-person detection system for epidemic prevention and control

In order to control the epidemic and complete the supervision of increasing population, we devised a kind of face detection system. This system detected face with MTCNN and then it detect whether the person wears the mask with MobileNet. Also we added non-standardized samples in the model training so that it can detect pedestrians who are not properly worn. The experimental results showed that the system can effectively identify the wearing of masks.

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Embedded Smart Car Security System on Face Detection

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2014.1240 ◽

2014 ◽

pp. 176-180

Author(s):

Vikram Kulkarni ◽

Viswaprakash Babu

Keyword(s):

Face Detection ◽

Detection System ◽

Security System ◽

Experimental Results ◽

Embedded Platform ◽

Web Camera ◽

The Face ◽

Car Security ◽

System Prototype

In this proposed embedded car security system, FDS(Face Detection System) is used to detect the face of the driver and compare it with the predefined face. For example, in the night when the car’s owner is sleeping and someone theft the car then FDS obtains images by one tiny web camera which can be hidden easily in somewhere in the car. FDS compares the obtained image with the predefined images if the image doesn’t match, then the information is sent to the owner through MMS. So now owner can obtain the image of the thief in his mobile as well as he can trace the location through GPS. The location of the car as well as its speed can be displayed to the owner through SMS. So by using this system, owner can identify the thief image as well as the location of the car This system prototype is built on the base of one embedded platform in which one SoC named “SEP4020”(works at 100MHz) controls all the processes .Experimental results illuminate the validity of this car security system.

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Dynamic Evaluation Model of the Default Risk of Online Loan Borrowers Based on K-means and SVM

Recent Advances in Computer Science and Communications ◽

10.2174/2666255814666210618111901 ◽

2021 ◽

Vol 14 ◽

Author(s):

Jian-Qiong Huang ◽

Wen-Long Guo ◽

Chou-Yuan Lee

Keyword(s):

Decision Support ◽

Default Risk ◽

Prevention And Control ◽

Credit Rating ◽

Evaluation Model ◽

Experimental Results ◽

Risk Prevention ◽

Healthy Development ◽

And Control ◽

Online Lending

Objective: With the development of Internet finance, the scale of peer-to-peer (P2P) online lending platforms has rapidly expanded. Additionally, the phenomenon of losses among online lending platforms and the problem of default by borrowers have emerged, greatly restricting the healthy development of online lending platforms. Therefore, it is necessary to dynamically set the credit rating of the borrowers according to the performance of the borrowers, and establish a default risk evaluation model for the borrowers of the online lending platform to promote the healthy development of the online lending platform. Method: This paper uses web crawler technology to obtain borrower information as the sample data, selects 17 core variables as explanatory variables, and utilizes project status as the target variable. First, according to the performance of the borrower, we use K-means to cluster, obtain a dynamic credit rating by calculation, and reset the rating to obtain new borrower information. Second, we determine the optimal parameters of the support vector machine algorithm through cross-validation and establish the best evaluation model for online loan borrowers' default risk. Finally, we conduct an experimental verification. Results: The classification accuracy of the proposed algorithm is better than that of decision trees and random forest, and the classification effect is the strongest. Conclusion: The experimental results show that the model has good stability and generalizability, and the research results provide dynamic decision support for early warnings of online lending platform risk and risk prevention and control; they can thus help in promoting the healthy development of online lending platforms. Conclusion: The experimental results show that the model has good stability and generalizability, and the research results provide dynamic decision support for early warnings of online lending platform risk and risk prevention and control; they can thus help in promoting the healthy development of online lending platforms.

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Device and Method for Large Size Aspheric Measurement

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.97-101.4324 ◽

2010 ◽

Vol 97-101 ◽

pp. 4324-4327

Author(s):

Yong Lu Zhu ◽

Yin Biao Guo ◽

Xiao Long Ke ◽

Lu Shuang Chen

Keyword(s):

Control System ◽

Measurement System ◽

Measurement Method ◽

Measurement Accuracy ◽

Detection System ◽

Experimental Results ◽

Aspheric Surface ◽

Large Size ◽

And Control

In this paper, the necessary instrument and control system called the “Large Size Four-axis Measurement System” which used for measuring large size aspheric surface have been designed and established. Then the development of semi-meridian measurement method on basis of large size four-axis detection system has been reported. The measurement paths were planed and the measurement program was performed on the IPC. The method can significantly reduced the align error caused by the aspheric center which is not coincidence with the rotation platform center. With the measurement system, an aspheric surface was successfully measured; whose radius of semi-meridian was 55 mm. The experimental results show that the developed methods are simple and rapid. The measurement accuracy can satisfy requirement of aspheric surface.

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Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Agriculture ◽

10.3390/agriculture11111062 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1062

Author(s):

Beibei Xu ◽

Wensheng Wang ◽

Leifeng Guo ◽

Guipeng Chen ◽

Yaowu Wang ◽

...

Keyword(s):

Face Detection ◽

Prevention And Control ◽

Research Work ◽

Meat Products ◽

Detection Algorithm ◽

Individual Identification ◽

Expression Recognition ◽

Average Precision ◽

Computer Vision Systems ◽

And Control

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

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