Lightweight Identification Method for Power Grid Equipment Based on Model Compression

The monitoring and maintenance of grid equipment have become increasingly crucial due to the continual progress in smart grid technology. Efficient identification technology for grid equipment is crucial for enabling equipment status monitoring and fault diagnosis, directly influencing the operational stability of the grid concerning precision and timely functionality. Nevertheless, the reliance of current image recognition methods on intricate models and extensive computational resources poses implementation challenges in resource-limited field environments, thereby restricting their use in operations such as drone-based power line inspections. In response to this obstacle, the paper introduces a streamlined identification approach for grid equipment through model compression. This method aims to uphold recognition precision while minimizing the computational workload and storage demands of the model, making it well-suited for integration into drone-based power line inspections. Introducing a target recognition network, this method integrates tailored multi-scale information for grid equipment and embeds an attention mechanism within the network to enhance the model’s capacity for identifying crucial features. Expanding on this approach, model compression techniques are utilized to condense the trained model. This process maintains accuracy by removing redundant weights, thereby shrinking the model’s size and computational complexity, ultimately achieving a lightweight network.