A Self-Mutual Learning Framework Based on Knowledge Distillation for Scene Text Detection

Knowledge distillation serves as a prevalent model compression strategy within scene text detection, enabling the transfer of learned representations from a high-capacity teacher architecture to a streamlined student counterpart. Building upon this concept, deep mutual learning alleviates dependence on the teacher model through interactive learning among student models. However, existing deep mutual learning networks inadequately address the complex redundant backgrounds and text feature distributions in scene text images, failing to effectively balance the trade-off between model performance and lightweight design. To address this issue, this paper proposes an improved self-mutual learning framework based on deep mutual learning. By employing a design that incorporates parallel multi-detection heads and interactive learning, the proposed approach simplifies the model training process while significantly improving detection accuracy. Specifically, the framework introduces a pruning mechanism that enables different detection heads to capture input features with varying degrees of sparsity. This not only reduces interference from redundant backgrounds but also leads to a more lightweight implementation. Moreover, varying feature sparsity among detection heads promotes more diverse knowledge exchange throughout mutual learning. This substantially boosts the distilled model’s resilience in intricate text environments. Comprehensive evaluations show that our approach achieves superior F-measure scores compared to leading knowledge distillation methods.