A Lightweight Perception Enhancement Network for Real-Time and Accurate Internal Surface Defect Detection of Cold-Drawn Steel Pipes

The detection of internal surface defects in cold-drawn pipes is challenging. In recent years, as the production demands for cold-drawn steel pipes steadily grow, there has been an urgent need for an efficient detection approach that balances accuracy and real-time performance in industrial environments. Although several existing deep learning-based methods have achieved high accuracy in surface defect detection, they often need substantial computational costs to extract rich feature representations, which inevitably slows down the inference process and lead to low detection efficiency. Moreover, internal defects of cold-drawn pipes typically exhibit challenges, which may further degrade the performance of existing models. To address these challenges, we propose a Lightweight Perception Enhancement Network (LPENet), to effectively balance efficiency and accuracy. Specifically, we introduce a Progressive Feature Extraction (PFE) backbone that enhances contextual perception from local to global scales. Furthermore, we design a Multi-Context Enhancement (MCE) module to enrich the feature representation and Boundary-Enhanced Aggregation (BEA) module to strengthen fine-grained feature awareness. In addition, we propose a Perception-Guided Fusion (PGF) strategy to facilitate interaction between shallow and deep features. We deploy LPENet in combination with a Pipe Internal Surface Detection (PISD) robot, achieving wireless and efficient defect detection in real-world steel pipe factories. Extensive experiments on the SSP2000 dataset, LPENet achieves the best balance between detection accuracy and efficiency. The source code is publicly available at https://github.com/VDT-2048/LPENet.