DSGNet: A Lightweight Network Integrating Depthwise Separable and Ghost Convolutions for Real-Time Surface Defect Segmentation

In industrial product manufacturing, the automated detection and localisation of surface defects are of significant importance for ensuring quality control. However, existing computer vision-based defect detection methods struggle to achieve both lightweight design and high accuracy on resource-constrained
embedded platforms, which limits their application in practical industrial detection environments. To address this issue, we propose DSGNet, a lightweight surface defect segmentation model, which serves as a core defect detection and localisation method for industrial inspection systems. The proposed model adopts an asymmetric encoder-decoder structure to simplify the overall architecture. We designed an efficient feature extraction network by using four
lightweight feature extraction units based on efficient convolutions. Furthermore, we introduce a hierarchical adaptive upsampling
fusion (HAFU) mechanism and a lightweight bidirectional multiscale strip attention (LBMSA) feature refinement module to effectively fuse and refine the multilevel features extracted from the encoder. We conducted comprehensive evaluations of DSGNet on three typical surface defect datasets: Neu-Seg,
MSD and MT. While maintaining an extremely low complexity with only 0.49 M parameters, DSGNet achieved impressive mIoU scores of 83.39%, 91.61% and 80.72% on three datasets, respectively. These results indicate that DSGNet is a promising solution that balances lightweight design and detection accuracy for industrial real-time detection systems, demonstrating strong potential for practical deployment. Our code is available at
https:// github. com/young -zyy/DSGNet.