Faster OreFSDet: A lightweight and effective few-shot object detector for ore images.

• A lightweight and effective few-shot detector is designed to address the over-fitting issue under limited labeled data for ore particle detection. • We introduce a support feature mining block to characterize the importance of semantic information. • A relationship guidance block is used to establish the correlation between support and query features for guiding the generation of precise candidate proposals. • The novel dual-scale semantic aggregation module is proposed to retrieve detailed features at different resolutions to contribute with the prediction process. 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 19 MB as well as being competitive at 50 FPS detection speed compared with general object detectors. [ABSTRACT FROM AUTHOR]