Feature Decoupling-Guided Annotation Framework for Surface Defects on Steel Strips

Abstract

Surface defect detection on steel strips is a critical step in quality control for industrial products. While existing research has made some progress in optimizing annotation strategies and improving efficiency, issues such as feature aliasing during the annotation process, the insufficient utilization of boundary information, and the inaccurate representation of complex defect patterns remain inadequately addressed. To tackle these challenges, this paper proposes an annotation optimization framework from the perspective of feature analysis. The framework decomposes defect features into geometric features and grayscale distribution features and, based on feature decoupling theory, classifies defects into three typical patterns: block, linear, and textured defects. For each pattern, the minimum annotation units that preserved essential features were designed, enabling the standardized representation of complex defects and precise boundary localization. Experiments on the NEU-DET dataset showed that this annotation framework improves the average mAP of six mainstream detection models by 4.9 percentage points, validating its effectiveness in enhancing the detection performance. Additionally, this paper introduces an Efficiency–Cost Ratio (ECR) evaluation metric to quantify the relationship between the annotation cost and performance improvement. The study found that block and linear defect detection achieved optimal performance with only 50% annotation effort. This research not only improved the performance of defect detection models but also quantified the annotation resource utilization efficiency, providing robust theoretical support and practical guidance for efficient defect detection in complex industrial scenarios.