A Surveillance Video Real-Time Analysis System Based on Edge-Cloud and FL-YOLO Cooperation in Coal Mine

Xu, Zhi; Li, Jingzhao; Zhang, Mei

doi:10.1109/access.2021.3077499

Cited by 42 publications

(15 citation statements)

References 41 publications

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“…Therefore, algorithms of single-stage detectors were introduced; these algorithms include SSD [127], YOLO [128], R-FCN [129], and Mask R-FCN [130], among others that eliminated the need for designing a set of anchor boxes [131]; such as CenterNet [132], RetinaNet [133], CornerNet [134], and their different variants. While these fast single-stage detectors often signi cantly compromise accuracy for achieving real-time detection, to date, only YOLO is faster yet more accurate than other alternatives [135]. In our paper, three different detection approaches were proposed to perform compliance inspections by detecting workers wearing PPE (safety helmet and safety vest) and workers who do not.…”

Section: Dataset and Methodologymentioning

confidence: 99%

Enhanced safety implementation in 5S + 1 via object detection algorithms

Shahin

Chen

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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Scholarly work points to 5S+1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6 th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6 th S in 5S+1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing Personal Protective Equipment (PPE).The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers' PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and veri es PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 Frames per Second (FPS), making it suitable for real-time detection.

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Section: Dataset and Methodologymentioning

confidence: 99%

Enhanced safety implementation in 5S + 1 via object detection algorithms

Shahin

Chen

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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show abstract

“…A more novel application of Computer vision models is its use in security systems as well as safety oversight networks. The sources presented in this section cover applications in detecting violent assaults [ 12 ] and mining personnel safety [ 3 ] to detecting survivors of severe natural disasters [ 113 ]. Most of these applications make use of RGB video and image cameras to perform detection and recognition.…”

Section: Application Based System Comparisonmentioning

confidence: 99%

“…Machine learning has become a ubiquitous feature in everyday life. From self-driving vehicles, facial recognition systems, and real-time interpretation of different languages, to security surveillance, smart home applications, and health monitoring, artificial intelligence has changed almost every society on earth [ 1 , 2 , 3 , 4 ]. Due to the extremely high computational requirements of machine learning models, until recently, the majority of these breakthroughs were implemented on high-power stationary computing systems.…”

Section: Introductionmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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“…To provide real-time object detection, YOLO—a neural network algorithm comprising convolution layers—is used for faster object detection. Further, [ 152 ] evaluates a fast and lightweight YOLO named FL-YOLO comprising depth-wise separable convolution layers. To migrate the tasks to the edge some kind of pre-processing is required.…”

Section: Anomaly Detection At Edge Devices Using Machine Learningmentioning

confidence: 99%

“…Furthermore, [ 2 ] studies FPGA system design to support edge-computing-based platforms. For the real-time video analysis, CNN layers are used in confluence with NVIDIA Jetson TX1 is the edge device [ 152 ]. Similarly, [ 53 ] uses Intel Neural Compute Stick 2 (NCS) as the edge device.…”

Section: Anomaly Detection On the Edge: Challenges And Future Researc...mentioning

confidence: 99%

Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

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A Surveillance Video Real-Time Analysis System Based on Edge-Cloud and FL-YOLO Cooperation in Coal Mine

Cited by 42 publications

References 41 publications

Enhanced safety implementation in 5S + 1 via object detection algorithms

Enhanced safety implementation in 5S + 1 via object detection algorithms

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Anomaly detection using edge computing in video surveillance system: review

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