Detection and recognition of stationary vehicles and seat belts in intelligent Internet of Things traffic management system

Wang, Zhenyan; Ma, Yongjie

doi:10.1007/s00521-021-05870-6

Cited by 24 publications

(12 citation statements)

References 21 publications

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“…End devices usually have network communication capabilities at the network's edge. For example, for individual users, the terminal device is a mobile phone, a laptop, or a tablet [12].…”

Section: Theoretical Basis and Methods Researchmentioning

confidence: 99%

Design of Edge Cloud Computing Recognition System for Intelligent Needs from the Perspective of Sports Psychology

Liu

2022

Preprint

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This paper intends to meet society’s demand for intelligent recognition system design and improve the performance of the behavioral action recognition system. Based on the previous research, the behavior recognition system is optimized. The edge Cloud Computing (CLO) technology is introduced. Also, the idea of sports psychology is integrated. The overall scheme of the behavior recognition system is designed. The accuracy, precision, and recall of the recognition system in different behaviors are analyzed by training the data set. In addition, the cloud-based behavior recognition design model is compared with other algorithms to analyze the algorithm advantages. Finally, the training and validation tests are performed on the training set of expression images using the behavior recognition system mode. It is found that the behavior recognition system model can show high accuracy in different environments, all above 80%. The results show that the edge CLO recognition system that meets intelligence needs has good accuracy, precision, and recall in both action recognition and image expression recognition. The recognition effect is the best. This paper aims to provide some ideas for the research on intelligent behavior recognition and put forward some references for the field expansion of edge CLO technology applications. Besides, the intelligence level of the behavior recognition system is improved through software design.

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“…End devices usually have network communication capabilities at the network's edge. For example, for individual users, the terminal device is a mobile phone, a laptop, or a tablet [12].…”

Section: Theoretical Basis and Methods Researchmentioning

confidence: 99%

Design of Edge Cloud Computing Recognition System for Intelligent Needs from the Perspective of Sports Psychology

Liu

2022

Preprint

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“…by researchers for identifying the number plates/license plates of vehicles. The identification of the owner of the vehicle via number/license plate is crucial in implementing traffic safety rules either by government authorities or other private/public organizations [20]. The major problem in detecting the registered vehicle number from its number/license plate is the format and language in which the required details are printed.…”

Section: Existing Solutions For Number/license Plate Detection Many T...mentioning

confidence: 99%

Image-based Seat Belt Fastness Detection using Deep Learning

Kapdi

Khanpara

Modi

et al. 2022

SCPE

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The detection of seat belts is an essential aspect of vehicle safety. It is crucial in providing protection in the event of an accident. Seat belt detection devices are installed into many automobiles, although they may be easily manipulated or disregarded. As a result, the existing approaches and algorithms for seat belt detection are insufficient. Using various external methods and algorithms, it is required to determine if the seat belt is fastened or not. This paper proposes an approach to identify seat belt fastness using the concepts of image processing and deep learning. Our proposed approach can be deployed in any organizational setup to aid the concerned authorities in identifying whether or not the drivers of the vehicles passing through the entrance have buckled their seat belts up. If a seat belt is not detected in a vehicle, the number plate recognition module records the vehicle number. The concerned authorities might use this record to take further necessary actions. This way, the organization authorities can keep track of all the vehicles entering the premises and ensure that all drivers/shotgun seat passengers are wearing seat belts.

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“…The essence of the Internet of Everything in edge cloud computing is that nodes realize information transfer and service between heterogeneous devices through a routing exchange mechanism. By integrating deep learning models into edge devices and using the powerful learning capabilities of deep learning models, edge devices can process image and text information more intelligently, which is a development direction of the intelligent IoT [17].…”

Section: Edge Cloud Computing Technologymentioning

confidence: 99%

Innovative Design of Environmental Visual Art Based on Edge Cloud Computing Technology under the Background of Artificial Intelligence

Han

Yao

2022

Preprint

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In the traditional artificial city planning, the greenway research generally takes the greenway layout or environmental effects as a single evaluation criterion. This work aims to solve the problem of weak commonality and difference of greenway landscape vision in the construction of urban style features. First, an image super-resolution reconstruction technology is proposed based on the Convolutional Neural Network to achieve precise processing of the urban greenway layout at a high level. Secondly, a one-time batch processing technology of images based on edge-cloud collaborative deep learning semantic segmentation of edge-cloud computing technology is proposed, which can quickly find out the problematic areas in greenway planning. Finally, the data set obtained by image processing is expressed by mapping software to analyze the commonness and difference of urban greenway landscape visual elements characteristics and composition diversity. The results show the spatial distribution sequence characteristics of different types of greenways and evaluate the relationship between the same type of greenway landscape visual services and their accessibility. The experimental results show that: (1) the CNN-based image super-resolution reconstruction technology designed is superior to the traditional technology in image processing and can more accurately identify the information in the image; (2) The image batch processing technology of edge-cloud collaborative deep learning semantic segmentation based on edge cloud computing technology can greatly improve the efficiency of image information processing, and has broader application prospects in image batch processing. This research aims to provide technical support for the upgrading and transformation of artificial intelligence technology in technology enterprises and reduce the adverse impact on urban construction caused by blind and one-sided urban environmental planning. Some method innovation and reference are provided for environmental visual art design innovation.

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Detection and recognition of stationary vehicles and seat belts in intelligent Internet of Things traffic management system

Cited by 24 publications

References 21 publications

Design of Edge Cloud Computing Recognition System for Intelligent Needs from the Perspective of Sports Psychology

Design of Edge Cloud Computing Recognition System for Intelligent Needs from the Perspective of Sports Psychology

Image-based Seat Belt Fastness Detection using Deep Learning

Innovative Design of Environmental Visual Art Based on Edge Cloud Computing Technology under the Background of Artificial Intelligence

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