The Electric_Data inspection dataset was constructed, and a PDD-SPCIE algorithm based on context information enhancement and multi-target semantic perception was proposed within the RT-DETR framework, in order to improve the detection accuracy of multi-category power defects in complex substation inspection scenarios. The LDDM-ResNet module was designed to extract high- and low-frequency detailed features through multi-kernel parallel convolution. The DA-Encoder module was constructed to enhance the representation of multi-scale and irregularly shaped targets by incorporating deformable attention and dynamic sampling. The LGCA module was introduced to fuse global and local features using atrous convolution and spatial depth transformation convolution. The original framework was improved from the aspects of feature representation, context modeling, and feature fusion, thus enhancing the unified detection ability for multiple defect categories. Experimental results showed that the proposed model achieved a precision of 0.819, a recall of 0.681, and an mAP₅₀ of 0.704 on the self-built dataset, and a precision of 0.719, a recall of 0.642, and an mAP₅₀ of 0.712 on the VOC dataset.