Abstract Slender objects have a large aspect ratio and are generally oriented, resulting in poor performance of current general detectors on slender object detection tasks. Therefore, an adaptive label assignment scheme for slender object detection is proposed in this paper. Specifically, the central axis prior to positive training samples is proposed to make the final position distribution of positive training samples more reasonable. Secondly, it is proposed that the number of positive training samples of slender objects could be further increased to solve the problem of positive training sample imbalance between slender objects and regular objects. Experimental results on the MS COCO dataset demonstrate the effectiveness of the proposed method.