@Article{cmc.2025.064191,
AUTHOR = {Zilu Liu, Hongjin Zhu},
TITLE = {RC2DNet: Real-Time Cable Defect Detection Network Based on Small Object Feature Extraction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {1},
PAGES = {681--694},
URL = {http://www.techscience.com/cmc/v85n1/63506},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2025.064191}
}