用双模型协作减少标注，提升密集零售图像检测精度
A Co-Training Semi-Supervised Framework Using Faster R-CNN and YOLO Networks for Object Detection in Densely Packed Retail Images
- Faster R-CNN与YOLO互换伪标签，兼顾定位与上下文
- 在SKU-110k数据集上达到89.3%的mAP，优于单一模型
- 适合需要频繁更新商品布局的零售自动化场景
本研究提出一种半监督协同训练框架，用于解决密集排列零售图像中因标注数据少、遮挡和重叠物多带来的检测难题。框架融合基于ResNet的Faster R-CNN（精准定位）与基于Darknet的YOLO（全局上下文感知），通过相互交换伪标签提升复杂场景下的检测精度。为增强分类鲁棒性，采用XGBoost、随机森林与SVM的集成策略，结合多种特征表示。使用元启发式算法优化超参数，提升各模型的精度与效率。该方法显著降低人工标注依赖，适应零售环境中频繁的商品与布局变动。在SKU-110k数据集上的实验表明，该框架性能优异，展现出在自动库存管理、产品监控及自助结账系统中的可扩展性与实用性。
原文摘要 · Abstract (English)
This study proposes a semi-supervised co-training framework for object detection in densely packed retail environments, where limited labeled data and complex conditions pose major challenges. The framework combines Faster R-CNN (utilizing a ResNet backbone) for precise localization with YOLO (employing a Darknet backbone) for global context, enabling mutual pseudo-label exchange that improves accuracy in scenes with occlusion and overlapping objects. To strengthen classification, it employs an ensemble of XGBoost, Random Forest, and SVM, utilizing diverse feature representations for higher robustness. Hyperparameters are optimized using a metaheuristic-driven algorithm, enhancing precision and efficiency across models. By minimizing reliance on manual labeling, the approach reduces annotation costs and adapts effectively to frequent product and layout changes common in retail. Experiments on the SKU-110k dataset demonstrate strong performance, highlighting the scalability and practicality of the proposed framework for real-world retail applications such as automated inventory tracking, product monitoring, and checkout systems.
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