To address the challenges of complex backgrounds and diverse target sizes in power operation environments, a lightweight edge-aware wearable detection algorithm for power operations is proposed. First, to mitigate background noise interference in power operation environment detection, a Multi-scale Edge Information Enhancement (MEIE) method is introduced. By extracting edge features at shallow layers, this method enables efficient fusion of edge information. In the hybrid encoder, the ShiftRepC3 module is designed to efficiently extract and fuse local features, while the DySample method improves upsampling to enhance reconstruction capability. Finally, an attention mechanism-based pruning strategy is introduced to reduce model parameters and computational costs, meeting the deployment requirements of edge devices in power operation scenarios. Experimental results demonstrate that, compared to the baseline RT-DETR model, the proposed method improves mAP50 by 2.8 and 2.1 percentage points on two datasets, and mAP50-95 by 2.6 and 1.5 percentage points, respectively, and maintains a low computational cost.