@article{Liu2025, 
author = {Zilu Liu and Hongjin Zhu},
title = {RC2DNet: Real-Time Cable Defect Detection Network Based on Small Object Feature Extraction},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {1},
pages = {681-694},
keywords = {Surface defect detection, computer vision, small object feature extraction, boundary feature enhancement},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.064191},
doi = {10.32604/cmc.2025.064191},
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.}
}