Region-Aware Guidance Training for pipeline segmentation in complex outdoor industrial environments

Accurate segmentation of insulated pipelines is essential for automated inspection and structural analysis in complex industrial environments. While UAV-based visual inspection is increasingly adopted, current systems often struggle with cluttered scenes and rely heavily on manual interpretation. This paper proposes Region-Aware Guidance Training (RAGT) for pipeline segmentation, a novel and modular deep neural network (DNN) training framework designed to enhance the performance and deployment readiness of state-of-the-art (SOTA) real-time region segmentation models in cluttered industrial settings. RAGT integrates two complementary components: Region-Augmented Knowledge Distillation (RAKD), which guides the model to focus on task-relevant regions, and Background-Aware Augmentation (BAUG), which improves generalization by increasing background diversity during training. Both modules can operate independently or jointly within the unified RAGT framework. Experiments demonstrate that RAGT achieves improvements of up to 8 units in challenging segmentation tasks.