In recent years, ensuring the secure operation of power equipment has become critically imperative. This study proposes ELA-YOLO, an enhanced YOLOv8-based object detection algorithm, to improve the identification and classification of critical power components. By integrating Efficient Multi-Scale Attention (EMA) into redesigned C2f modules (C2f_EMA), the backbone network achieves dynamic multi-scale feature fusion. The neck network is further optimized through Asymmetric Padding Convolution (APConv) in C2f_AP modules, enhancing spatial feature integration. Additionally, the Large Selective Kernel (LSK) attention mechanism strengthens context-aware feature extraction capabilities. Experimental results demonstrate that ELA-YOLO outperforms YOLOv8s with a 2.8% improvement in mAP?? while incurring only a 4.5% computational overhead, establishing an optimal balance be-tween detection accuracy and operational efficiency for real-world power equipment inspection scenarios.