A Moving Object Detection Scheme based on Video Surveillance for Smart Substation

Wang, Yun; Zhang, Jiangxuan; Zhu, Lingfeng; Sun, Zengqi; Lu, Jun

doi:10.1109/icsp.2018.8652316

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…Some studies have used a combination of traditional computer vision algorithms [75]. In [76], the adaptive motion estimation segmentation (AMES) and the proposed sequential outline separation (SOS) methods were used to detect multiple moving objects.…”

Section: Motion-based Object Detectionmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

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Section: Motion-based Object Detectionmentioning

confidence: 99%

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir,

Kim

2023

Electronics

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“…They evaluated this algorithm on private datasets, which revealed that its mean average precision (mAP) was 56.9%. In addition, Wang et al [7] detected moving objects with a Gaussian-model foreground extraction and image dithering approach, which they used to identify dynamic objects in video surveillance footage from smart substations.…”

Section: Related Researchmentioning

confidence: 99%

“…The third loss function is shown in (6), where and represent the score loss function and position loss function, respectively, and N represents the number of grid points. The solutions of and are shown in (7) and (8).…”

Section: Loss Function Selectionmentioning

confidence: 99%

“…Presently, however, there remains a large difference between traditional and smart substations [4]. Although many intelligent algorithms exist for safety helmet detection [5], personnel dress code detection [6], and moving object detection [7] in substations, they are only adequate for providing early warning of specific types of events, and their generalizability to other scenarios is very poor. The need for smart substations is growing as the role of substations in the electric power system is increasingly crucial.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Fusion Object-Detection Algorithm for Smart Substation System, 1-7.

Zhang¹

2023

International Journal of Power and Energy Systems

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Machine learning is playing an increasingly important role in smart substation systems. Object detection algorithms are commonly used in smart substations for procedures, such as helmet detection and personnel clothing inspection. However, object detection algorithms are inadequate for solving complex smart substation scenarios because of their poor generalisation ability. Thus, we introduce an intelligent fusion algorithm named YYSF-4 that has good generalisation ability. YYSF-4 comprises You Only Look Once (YOLO) V1, YOLO V3, a single-shot multi-box detector, and fastoriented text spotting, and is suitable for use in smart substations. We use real images from substations as a dataset to verify the effectiveness of the YYSF-4 in four scenarios: helmet detection and recognition, personnel clothing detection and identification, personnel detection and identification, and bill detection and recognition. The experimental results show that the mean average precision (mAP) of YYSF-4 in the above four scenarios is higher than the mAPs of other baseline algorithms.

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“…Designing remote video monitoring system for selfassembled substations, which can remotely monitor the operation status of the equipment in the substation; some researchers proposed to study the one-touch programmed control technology based on video integration and intelligent analysis [12], where video integration technology and intelligent analysis technology are applied to programmed control power systems to improve the control performance of many devices in power systems. Some scholars study intelligent analysis of substation video based on image processing [13] and apply image processing technology to intelligent analysis of substation video to improve the performance of substation equipment status analysis, and all the above studies apply visualization technology to substation equipment monitoring, and all achieve certain results [14,15]. It can be seen that image fusion techniques are mostly used in relevant visualization and monitoring studies, and the existing image fusion techniques include multisensor information fusion techniques, multiple gray-level image fusion techniques, wavelet transform-based image fusion techniques, etc., but jagged and dark-edge situations occur when the abovementioned image fusion techniques are applied to monitoring the vulnerability of high-voltage electrical equipment in substations [16].…”

Section: Introductionmentioning

confidence: 99%

Visual Monitoring Technology for Substation Vulnerable High-Voltage Electrical Equipment Based on ISSA-LSTM Deep Learning Model Coupling Video Overlay Algorithm

Wang

Ding

Tan

2022

Computational Intelligence and Neuroscience

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To enhance the visualization effect of substation high-voltage electrical equipment vulnerability, this study proposes an ISSA-LSTM coupled video overlay algorithm-based substation high-voltage electrical equipment vulnerability visualization and monitoring model. Using the improved α blending algorithm combined with the inverse sampling of video background color, overlaying visible video as well as infrared video, using the improved adaptive weighted two-dimensional principal component analysis (W2DPCA) to fuse the base layer, selecting the detail layer as the final detail layer, obtaining the final fusion frame, and realizing the visualization and monitoring of substation high-voltage electrical equipment vulnerability, and introducing the improved sparrow search algorithm (ISSA) to establish long and short-term memory network prediction model to reduce the prediction error and improve the monitoring accuracy rate. The experimental results show that the monitoring frames obtained by this method can reflect rich details of substation high-voltage electrical equipment, and the texture color and equipment edge contrast are enhanced to facilitate accurate determination of substation high-voltage electrical equipment vulnerability, and the prediction accuracy of ISSA-LSTM model is as high as 99.85%.

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A Moving Object Detection Scheme based on Video Surveillance for Smart Substation

Cited by 8 publications

References 5 publications

A Survey of Video Surveillance Systems in Smart City

A Survey of Video Surveillance Systems in Smart City

An Intelligent Fusion Object-Detection Algorithm for Smart Substation System, 1-7.

Visual Monitoring Technology for Substation Vulnerable High-Voltage Electrical Equipment Based on ISSA-LSTM Deep Learning Model Coupling Video Overlay Algorithm

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