基于并联双注意力的轻量级小样本矿石粒度检测OA

To address the high computational complexity,limited feature robustness,and constrained clas-sifier performance of conventional object detection methods in ore particle size detection,a few-shot object detection approach was proposed to reduce annotation cost and improve generalization under data-scarce conditions.The proposed method was built upon the CenterNet2 framework and employed a lightweight VoVNet as the backbone to ensure detection efficiency.A parallel dual-attention feature fusion module was designed as the core component.Specifically,a channel cross-attention module was introduced to re-calibrate channel-wise feature responses,while a spatial group-attention module emphasized discriminative target regions.The coordinated operation of the two modules enhanced the fusion of task-relevant features and provided effective guidance for query image detection in few-shot scenarios.Experimental results on an ore dataset show that the proposed model achieved an average precision(AP)of 55.2%,with AP50 and AP75 reaching 78.5%and 66.9%,respectively.The inference speed reached 57 frames per second(FPS),while the attention module required only 16.1 M parameters,indicating a favorable trade-off be-tween accuracy and efficiency.Experimental results demonstrate that the proposed method effectively en-hances the perception performance of few-shot ore particle size detection.Moreover,it possesses high po-tential for edge deployment,providing a reliable technical solution for real-time detection challenges in smart mines under computation-constrained conditions.