Design of multi-mode intelligent system architecture for surface defect detection of steel based on cloud technology

This research establishes an advanced cloud-native multimodal intelligent system architecture dedicated to automated detection and real-time monitoring of steel surface defects. The architecture ingeniously integrates the advantages of edge computing, cloud computing, and mobile computing technologies, achieving efficient processing and precise analysis of industrial big data through advanced deep learning algorithms. The main innovative contributions of this research include: (1) Optimized deployment of improved YOLOv5-based algorithm systems, including YOLOv5-efficient net and YOLOv5-mobile net series lightweight models, which significantly enhance defect recognition capabilities in complex industrial environments; (2) Meticulously designed microservice architecture and distributed message queue system, realizing high availability and dynamic scalability; (3) Pioneering construction of a multi-dimensional intelligent result distribution ecosystem, encompassing cross-platform services (WeChat, email), real-time push notifications (iMessage), and mobile application alerts (Android applications), ensuring seamless and timely delivery of detection results to different end users. In rigorous evaluations on a large-scale self-built steel defect dataset, the system demonstrates good performance across key metrics including detection accuracy, recall rate, and processing speed. Practical deployment has validated the system's high reliability and robust adaptability in industrial production environments. This research not only provides a feasible technical solution for the intelligent transformation of the steel industry but also establishes a forward-looking technical paradigm for intelligent manufacturing in the context of Industry 4.0, laying a solid foundation for achieving unmanned, high-quality modern industrial production.