Performance Evaluation of the SCN++ Model for Structural Crack Detection in Edge Computing Environments

This study proposes a lightweight crack-segmentation model optimized for industrial and edge-computing environments, where both high accuracy and real-time inference are required. Conventional convolution-based and U-Net-based crack segmentation models offer relatively simple architectural designs, but often suffer from limited boundary precision or an unfavorable accuracy–efficiency trade-off. Swin Transformer-based approaches can model broader contextual information but may still show poor segmentation quality relative to their computational cost in fine crack analysis. To address these limitations, we propose the Stabilized Crack Network++ (SCN++), a U-Net backbone crack segmentation network that integrates edge fusion, hybrid loss with deep supervision, exponential moving average (EMA)-based stabilization, and lightweight post-processing. The model was trained and evaluated on 40,000 concrete surface images, including 20,000 crack images and 20,000 non-crack images, using quantitative metrics such as intersection over union (IoU), Dice coefficient, frames per second (FPS), giga floating-point operations (GFLOPs), and the number of parameters, together with overlay-based qualitative analysis. Compared with the CNN, U-Net, and Swin Transformer baselines, SCN++ achieved the best overall balance between segmentation accuracy and computational efficiency, with an IoU of 0.7346, a Dice coefficient of 0.8457, 35.09 FPS, 8.45 GFLOPs, and only 2.22 M parameters. These results demonstrate that SCN++ effectively mitigates the conventional accuracy–efficiency trade-off and is a strong candidate for practical structural crack segmentation in edge-computing environments.