Research and Application of Key Technology of Safety Situational Awareness for the Whole Process of Grid Infrastructure Construction Based on Edge-Side Scene Recognition and Knowledge Fusion
Published Online: Feb 03, 2025
Received: Sep 06, 2024
Accepted: Dec 18, 2024
DOI: https://doi.org/10.2478/amns-2025-0004
Keywords
© 2025 Bo Chen et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Personnel violations in power construction sites pose significant safety risks and may lead to serious accidents. This paper proposes a method based on multi-sensor fusion and deep neural networks to recognize staff violations and fully understand on-site personnel behavior. By integrating data from multiple sensors, including visual and distance sensors, this method allows for precise personnel positioning and detailed behavior analysis. Fine-grained behavior analysis and recognition can be achieved by incorporating 3D point cloud data to obtain three-dimensional spatial information of personnel, and combining texture features of visible light images. The method first performs pre-processing and feature extraction on multimodal data, followed by integrating information from both data sources using a fusion strategy, and constructing a behavior recognition model using deep neural networks. Experimental results in complex real-world scenarios show that this method significantly outperforms single data source approaches in terms of recognition accuracy and robustness, effectively enhancing personnel behavior recognition in complex ground environments.