Lightweight and Accurate YOLOv7-Based Ensembles With Knowledge Distillation for Urinary Sediment Detection

Urine sediment analysis plays an important role in evaluating kidney function. In addition to improving detection accuracy, reducing model size is also a key challenge, especially when considering deployment on medical devices where computational resources are limited. To address these demands, we propose a lightweight and accurate detection framework that combines YOLOv7-based ensemble learning with feature-based knowledge distillation. In our approach, two attention-enhanced YOLOv7 variants are used as teacher models, which transfer their feature representations to compact YOLOv7-tiny student models. These student models are then integrated using weighted boxes fusion to further enhance detection performance. Evaluated on a real-world urinary sediment dataset, the proposed framework achieves 84.6% precision, 88.1% recall, and a mAP@0.5 of 0.920. These results represent an improvement of more than 3% over the baseline YOLOv7-tiny and slightly outperform the state-of-the-art YOLOv7-CBAM teacher model. Moreover, the model size is reduced to approximately one-third of YOLOv7-CBAM, significantly improving its suitability for deployment in resource-constrained environments. Ablation studies further confirm the complementary strengths of the ensemble design and suggest that additional performance gains may be achievable through more adaptive fusion strategies.