Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems. However, existing methods struggle with small target sizes, complex backgrounds, low-quality image acquisition, and interference from contamination. To address these challenges, this paper proposes the Real-time Cable Defect Detection Network (RC2DNet), which achieves an optimal balance between detection accuracy and computational efficiency. Unlike conventional approaches, RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids, multi-level feature fusion, and an adaptive weighting mechanism. Additionally, a boundary feature enhancement module is designed, incorporating boundary-aware convolution, a novel boundary attention mechanism, and an improved loss function to significantly enhance boundary localization accuracy. Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision, recall, F1-score, mean Intersection over Union (mIoU), and frame rate, enabling real-time and highly accurate cable defect detection in complex backgrounds.