Research on Intelligent Detection Method for Operation and Maintenance Violations of Power Distribution Equipment Based on YOLOv12
DOI:
https://doi.org/10.4108/eetsis.10801Keywords:
Intelligent detection method, O&M violation, PDE, YOLOv12, Deep Learning, Object detectionAbstract
INTRODUCTION: With the emergence of new equipment and technologies, the difficulty of operation and maintenance (O&M) of power distribution equipment (PDE) has been continuously increasing. Traditional manual supervision and monitoring methods have been unable to meet the requirements of real-time performance and accuracy.
OBJECTIVES: In order to effectively reduce operational safety risks, we propose an intelligent O&M violation detection method.
METHODS: This paper optimizes the architecture of YOLOv12 and constructs three models: a security tool violation carrying recognition model, a general violation operation behavior recognition model, and a specific task violation operation behavior recognition model, this paper also uses the 3D electronic fence and real-time acquisition of each operator's 3D joint coordinates, and predicts the 3D joint coordinates of operation and maintenance personnel based on the Kalman filter.
RESULTS: The method achievies accurate detection of O&M violations. In addition, this paper successfully establishes a 3D electronic fence for the O&M environment of PDE, and also achieves the recognition and early warning of violations related to spatial locations.
CONCLUSION: The intelligent analysis and evaluation system for power distribution equipment operation and maintenance safety based on multimodal data fusion developed based on this method has been deployed and applied in the PDE O&M environment, achieving intelligent recognition of violations in power distribution equipment operation and maintenance and significantly improving the level of intelligence in on-site safety control.
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