Zilu Liu1,#, Hongjin Zhu2,#,*
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 681-694, 2025, DOI:10.32604/cmc.2025.064191
- 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 More >
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