High-Precision Surface Crack Detection for Rolling Steel Production Equipment in ICPS

In industrial cyber–physical systems (ICPS), real-time condition monitoring of wear-prone components of steel rolling production equipment is a key scenario for predictive maintenance. Machine vision-based crack detection can quickly identify critical damage and prevent unplanned downtime. However, the harsh working environment poses difficulties for data collection, a large amount of noise tends to contaminate surface crack images, and complex surface crack morphology affects the recognition accuracy. The real-time and accuracy performance of traditional crack detection algorithms are hard to meet the requirement of industrial applications. To tackle this challenge, a high-precision surface crack detection architecture for rolling steel production equipment based on image semantic segmentation is proposed. First, a coordinate attention-deep convolution generative adversarial networks (CA-DCGANs)-based data augmentation method is proposed to augment the original data set with high quality. Second, a crack detection model based on multiscale learning efficient spatial pyramid network (MLESPNetV2) is proposed. It effectively improves detection accuracy to obtain semantic information strongly correlated with crack using multiscale modeling and attention mechanism. Third, A semi-supervised learning method based on multiscale learning efficient spatial pyramid-generative adversarial network (MLESP-GAN) is proposed to solve the problem of insufficient labeled data and unstable training process. Finally, extensive experimental results on KolektorSDD and CAS-Crack data sets demonstrate that the proposed MLESPNetV2 significantly improves accuracy and real-time performance compared with the benchmark model. It is therefore suitable for deployment in industrial sites for real-time health monitoring of industrial equipment.