Open Access

ARTICLE

RC2DNet: Real-Time Cable Defect Detection Network Based on Small Object Feature Extraction

Zilu Liu^1,#, Hongjin Zhu^2,#,*

1 School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, 213001, China
2 School of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, China

* Corresponding Author: Hongjin Zhu. Email:
# These authors contributed equally to this work

Computers, Materials & Continua 2025, 85(1), 681-694. https://doi.org/10.32604/cmc.2025.064191

Received 07 February 2025; Accepted 17 March 2025; Issue published 29 August 2025

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.

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Keywords

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