Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers
- Real-time pose estimation: This paper proposes a method that allows for the pose of a worker to be estimated in real time. This is an important contribution to estimating the pose of workers in the field and quickly detecting dangerous situations.
- Accuracy: The method proposed in this paper shows high accuracy in pose estimation through a combination of OpenPose algorithm and ResNet-50. This aids the real-time detection of a worker’s status by accurately estimating a worker’s pose.
- Safety prevention and accident detection: The method proposed in this paper monitors the pose of workers in real time and also detects safety hazards that may occur in the work environment. This helps workers perform their tasks in a safer environment while also enabling rapid response and rescue in the case of accidents.
- Building an AI-based safety system: The proposed system can be utilized to enhance safety in an enterprise or field. By analyzing and evaluating the posture and behavior of workers in real time, the posture estimation technology based on AI can contribute to building a more systematic and efficient safety management system.
- Efficient resource utilization: In this study, ResNet-50 was applied to enable real-time pose estimation at a faster speed compared to the existing OpenPose. This improved speed and accuracy can be seen as an important contribution to resource efficiency. Especially when estimating the pose of many workers simultaneously in real-time in a large work environment, the efficiency of the system is crucial.
- Optimization for industrial settings: By using a dataset of workers in an industrial setting to train and evaluate the model, this study yields optimized results for real-world work environments, which is an important contribution that increases its applicability in real-world settings.
- Future applications: The techniques in this study can serve as a basis for safety and accident prevention for field workers, and future applications can be actively researched. For example, it could be applied to posture estimation and accident prevention systems not only in industrial sites but also in various fields such as healthcare, sports, and security.
2. Related Work
2.2. Multi-Person Pose Estimation
2.2.1. Top-Down Method
2.2.2. Bottom-Up Method
2.2.3. Single-Stage Methods
3. ResNet-50 Based Real-Time Pose Estimation
3.1. Overall Structure
3.2. Feature Extraction Stages
3.3. Operation Flow
3.4.1. Construction Sites
3.4.2. Oil and Gas Industry
3.4.3. Power Supply Industry
4.1. Experiment Environments
4.2. Performance Metrics
- Red: Line connecting the head and neck;
- Orange: Line connecting the right shoulder and right elbow;
- Yellow: Line connecting the right elbow and right wrist;
- Lime Green: Line connecting the left shoulder and left elbow;
- Green: Line connecting the left elbow and left wrist;
- Cyan: Line connecting the right thigh, right calf, and right foot;
- Blue: Line connecting the left thigh, left calf, and left foot;
- Pink: Line connecting the nose and right eye;
- Purple: Line connecting the nose and left eye.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|OS||CUDA||cMa + ke||cuDNN||OpenCV||Python||Visual Studio||GPU|
|Windows 11 Home||v11.8||v3.26.3||v8.2.4||v4.5.4||v3.9.13||2019 Enterprise||NVIDIA GeForce|
RTX 3070Ti Laptop GPU
|OpenPose based on ResNet-50||0.91||0.92||0.92||0.90||0.91||0.89|
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Lee, J.; Kim, T.-y.; Beak, S.; Moon, Y.; Jeong, J. Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers. Electronics 2023, 12, 3513. https://doi.org/10.3390/electronics12163513
Lee J, Kim T-y, Beak S, Moon Y, Jeong J. Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers. Electronics. 2023; 12(16):3513. https://doi.org/10.3390/electronics12163513Chicago/Turabian Style
Lee, Jieun, Tae-yong Kim, Seunghyo Beak, Yeeun Moon, and Jongpil Jeong. 2023. "Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers" Electronics 12, no. 16: 3513. https://doi.org/10.3390/electronics12163513