Swin Transformer-Based Real-Time Multi-Tasking Image Detection in Industrial Automation Production Environments

Automated production plays a vital role in the long-term development of industrial enterprises, and automated production has high requirements for defect detection of industrial parts. In this study, we construct a complex atom network based on Swin Transformer—selected for its window-based multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) mechanisms, which enable efficient cross-window feature interaction and reduce computational complexity compared to vanilla Transformer or CNN-based methods in multi-task scenarios—and after repairing and recovering the abnormally generated and randomly masked images in the industrial automated production environment, we utilize the discriminative sub-network to achieve real-time abnormality image detection and classification. Then, the loss function optimization model is used to construct a real-time multi-task image detection model (MSTUnet) and design a real-time detection system in the industrial automation production environment. In the PE pipe image defect detection for industrial automated production, the average recognition rate of this paper’s detection model for six kinds of defects can reach 99.21%. Practical results show that the product excellence rate and qualification rate in the industrial automated production line equipped with this paper’s detection system reached 15.32% and 91.40%, respectively, and the production efficiency has been improved. The real-time multi-task image inspection technology and system proposed in this paper meet the requirements of industrial production for accurate, real-time and reliable, and can be practically applied in the industrial automation production environment, bringing good economic benefits.