Load Equipment Segmentation and Assessment Method Based on Multi-Source Tensor Feature Fusion

The state monitoring of power load equipment plays a crucial role in ensuring its normal operation. However, in densely deployed environments, the target equipment often exhibits low clarity, making real-time warnings challenging. In this study, a load equipment segmentation and assessment method based on multi-source tensor feature fusion (LSA-MT) is proposed. First, a lightweight residual block based on the attention mechanism is introduced into the backbone network to emphasize key features of load devices and enhance target segmentation efficiency. Second, a 3D edge detail feature perception module is designed to facilitate multi-scale feature fusion while preserving boundary detail features of different devices, thereby improving local recognition accuracy. Finally, tensor decomposition and reorganization are employed to guide visual feature reconstruction in conjunction with equipment monitoring images, while tensor mapping of equipment monitoring data is utilized for automated fault classification. The experimental results demonstrate that LSE-MT produces visually clearer segmentations compared to models such as the classic UNet++ and the more recent EGE-UNet when segmenting multiple load devices, achieving Dice and mIoU scores of 92.48 and 92.90, respectively. Regarding classification across the four datasets, the average accuracy can reach 92.92%. These findings fully demonstrate the effectiveness of the LSA-MT method in load equipment fault alarms and grid operation and maintenance.