面向电网设备缺陷识别的多模态感知模型的构建与优化

Construction and Optimization of Multimodal Perception Model for Defect Identification of Power Grid Equipment

在电网设备的维护和管理中,缺陷识别是预防设备故障的关键环节。然而,传统的缺陷识别方法主要依赖于可见光和红外数据的处理,会面临识别精度低、泛化性差等问题。针对这些挑战,本研究提出了一种基于Transformer的多模态缺陷识别方法。该方法通过整合可见光、红外数据等多种数据模态,克服了单一模态数据的局限性,为缺陷识别提供了更为丰富的信息。利用U-Net网络结构,有效提取了电网设备图像中的特征信息,为后续的缺陷识别提供了坚实的基础。同时,对Transformer结构进行了优化,提高了其在电网设备缺陷识别任务中的性能,实现了对变压器、套管、断路器等电网设备的精准定位和缺陷识别。实验结果表明,该方法明显提升了缺陷识别的效果,不仅提高了识别精度,还增强了模型的鲁棒性,使得模型能够更好地适应不同设备和缺陷类型的识别任务。

In the maintenance and management of power grid equipment,defect identification is a key link in preventing equipment failure.However,traditional defect identification methods mainly rely on the processing of visible light and infrared data,and will face problems such as low recognition accuracy and poor generalization.In response to these challenges,this study proposes a multi-modal defect identification method based on Transformer.By integrating multiple data modalities such as visible light and infrared,the limitations of single modal data are overcome and more abundant information is provided for defect identification;the U-Net network structure is used to effectively extract feature information from power grid equipment images,which provides a solid foundation for subsequent defect identification.The Transformer structure has been optimized to improve its performance in power grid equipment defect identification tasks,achieving precise positioning and defect identification of power grid equipment such as transformers,bushings,and circuit breakers.Experimental results show that this method has achieved significant improvement in the defect recognition task.It not only improves the recognition accuracy,but also enhances the robustness of the model,allowing the model to better adapt to the recognition tasks of different equipment and defect types.