Environment-Aware Continual Transfer Learning for Real-Time Defect Detection in 3D Printing

In 3D printing material defect detection, environmental variations frequently induce false positives and missed detections by automated systems. Current research addresses dynamic environments by fine-tuning models during the detection phase. However, existing methods suffer from delayed adaptation: models require prolonged iterations to achieve stable performance in new environments. Furthermore, noise-contaminated pseudo-labels during domain adaptation exacerbate error accumulation and catastrophic forgetting, leading to severe performance degradation of the model. To address these challenges, we propose a robust and rapid adaptive detection framework tailored for dynamic environments. First, we innovatively employ the Gram matrix of model feature layers to quantify environmental shifts, endowing the model with real-time environmental awareness. Second, the dynamically maintained sample buffer ensures that stored samples satisfy three critical properties: pseudo-label reliability, class-balanced distribution, and diverse environmental representation. This mechanism selects samples most representative of new environments for fine-tuning, significantly accelerating adaptation. Experimental results demonstrate that our method achieves superior detection accuracy on real-world 3D printing material datasets under complex scenarios (e.g., sudden illumination changes and environmental shifts). Compared to baseline Test-Time Adaptation (TTA) methods, it exhibits enhanced adaptability and robustness.