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In the ore beneficiation process, large block detection is necessary to ensure production safety. This typically involves identifying oversized ore on the conveyor belt and preventing material blockage accidents in the transfer buffer bin between the ore feeding belt and the ore receiving belt. Methods based on deep learning can learn to construct complex features from a large amount of data, but they also require a large number of hand-made datasets for training. Although the existing few shot detection methods for ore images reduce the cost of manual labeling, the corresponding detection performance is insufficient. This article mainly explores how to improve the performance of the detector under the ore image detection task in the case of few labeled images. First, a shot enhancement block is proposed to enhance the valuable foreground information for higher-quality support features. Subsequently, we present a dual-attention region proposal network that effectively leverages support features to enhance the precision of generating candidate proposals. Finally, we propose a lightweight multi-relational detector to effectively evaluate the relationship between query and support proposals, leading to a substantial enhancement in guidance performance. The proposed few-shot object detector (FS-OreDet) achieves the best detection results with state-of-the-art methods with an average precision (A P) of 55.1, a speed of 57 frames per second (F P S), and a model size of only 17 M B. Furthermore, our framework adeptly captures the feature information of ore images with substantial data. The detector's accuracy achieves a significant improvement of 14% in A P. Compared with general object detectors, the performance of the detector ranks first and meets the requirements for outdoor scene deployment. • Proposing a stronger few-shot object detector for ore images via feature enhancement and relationship exploration. • Some components are designed for boosting few-shot object detectors of ore images. • Extensive experiments on the ore dataset and theoretical analyses are also added to verify the superiority of the proposed detectors. [ABSTRACT FROM AUTHOR]