Progressive Knowledge Distillation for Edge‐Deployable Solder Joint Segmentation

ABSTRACT High‐precision segmentation of solder joint quality is vital for reliable electronics manufacturing, especially in flexible printed circuit ribbon cables, where even subtle defects can trigger severe functional failures. To address the fundamental challenge of model capacity gap in knowledge distillation for resource‐constrained devices, we present Solder‐Yolo, a lightweight instance segmentation framework with three core technical contributions: (1) a module‐level pruning strategy that reduces computational complexity by 32.8% and parameters by 86.2% compared to YOLOv8n; (2) a progressive knowledge distillation framework that bridges the capacity gap between heavy teachers and lightweight students through intermediate knowledge refinement—where YOLOv8l‐seg acts as a ‘knowledge translator’ to make complex features digestible for the compact model; and (3) a hierarchical context attention module that generates spatial‐channel aware feature maps as augmented knowledge to compensate for representational limitations induced by pruning. Extensive experiments on our newly curated industrial dataset show that Solder‐Yolo achieves 96.7% precision and 91.3% mAP, outperforming the baseline by 8.6% in precision while maintaining real‐time inference speed. The progressive distillation approach demonstrates a 3.4% improvement in mAP@50‐95 over direct distillation, validating its efficacy in mitigating the capacity gap problem for edge deployment.