Steel Strip Surface Defect Detection Method Based On The Uncertainty Region Fusion And Digital Twin Model Establishment

Abstract As a core component in steel production, the steel strip is prone to surface defects during the continuous production process due to its complex conditions. To achieve high-precision real-time monitoring of its surface quality and efficient quality control, the steel strip surface defect detection method is proposed based on uncertainty region fusion and digital twin model establishment. Specifically, the YOLOv8 and SSD models pre-trained on public defect datasets are used for independent surface detection, obtaining initial defect regions with confidence scores and uncertain detection results. To address single-model detection uncertainty and enhance detection accuracy, the decision-level uncertainty region fusion method is designed to fuse initial regions and uncertainty information based on region overlap and confidence, acquiring maximum reliable defect regions for surface defect recognition and localization. Different from existing state-of-the-art steel strip defect detection methods, the proposed method innovatively introduces uncertainty perception and diagonal equal-area decision-level fusion. It effectively reduces unreliable bounding box predictions and single-model detection bias, and further realizes high-confidence defect data interaction and virtual-real synchronization with digital twin framework. To evaluate the performance of the proposed method, the public datasets and experimental steel strip defect datasets are utilized with several defects. The results demonstrate that the proposed method yields consistent and significant improvements in detection accuracy, recall, and overlap rate on both the public NEU dataset and the self-built dataset, which verifies the value for steel strip quality control and intelligent manufacturing.