For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from severe over-fitting with scarce labeled data. Despite their ability to eliminate over-fitting, existing few-shot object detectors encounter drawbacks such as slow detection speed and high memory requirements, making them difficult to implement in a real-world deployment scenario. To this end, we propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection with only a few samples for ore images. First, the proposed support feature mining block characterizes the importance of location information in support features. Next, the relationship guidance block makes full use of support features to guide the generation of accurate candidate proposals. Finally, the dual-scale semantic aggregation module retrieves detailed features at different resolutions to contribute with the prediction process. Experimental results show that our method consistently exceeds the few-shot detectors with an excellent performance gap on all metrics. Moreover, our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed compared with general object detectors. The source code is available at https://github.com/MVME-HBUT/Faster-OreFSDet.

翻译：针对矿石粒度检测问题，获取大量高质量矿石标注数据既耗时又昂贵。通用目标检测方法在标注数据稀缺时往往会出现严重的过拟合现象。现有小样本目标检测器虽能消除过拟合，却存在检测速度慢、内存需求高等缺陷，难以在实际部署场景中应用。为此，我们提出一种轻量级且有效的小样本检测器，仅需少量矿石图像样本即可实现与通用目标检测相媲美的性能。首先，所提出的支持特征挖掘模块刻画了支持特征中位置信息的重要性。其次，关系引导模块充分利用支持特征来指导生成精确的候选提议。最后，双尺度语义聚合模块在不同分辨率下检索细节特征以辅助预测过程。实验结果表明，我们的方法在所有指标上均显著超越现有小样本检测器。此外，相较于通用目标检测器，本方法实现了19MB的最小模型体积，同时达到50 FPS的竞争性检测速度。源代码已开源至https://github.com/MVME-HBUT/Faster-OreFSDet。