Violation detection in power operation sites based on multi-scale detection and few-shot learning

Safety supervision at power operation sites is critical for ensuring worker safety and maintaining a reliable electricity supply. However, existing safety violation detection methods are constrained by limited labeled data, poor performance on small-object detection tasks, and interference from complex backgrounds.

To overcome these challenges, this study proposes a framework that integrates multi-scale object detection with few-shot learning. A multi-scale feature extraction module is designed based on a feature pyramid network and channel attention mechanisms to enhance the perception of small objects. In addition, a few-shot learning framework incorporating a meta-learning strategy is introduced to address the scarcity of labeled safety violation samples and improve the model's adaptability to new tasks with limited training data.

Experimental results demonstrate that the proposed method consistently outperforms existing approaches across multiple evaluation metrics. The framework achieves notable improvements in small-object detection accuracy and few-shot learning performance, resulting in enhanced detection accuracy, robustness, and generalization capability.

The integration of multi-scale feature extraction and few-shot learning effectively addresses the challenges of safety violation detection in power operation environments. The proposed framework provides a practical and reliable solution for intelligent safety monitoring and has significant potential for real-world deployment in power operation sites.