For the case that one target may generate multiple detections per scan, an extended method within the labeled random finite sets framework is proposed. A new update equation is derived based on the delta-generalized labeled mult- Bernoulli(delta-GLMB) filter and multi-detection model. The hypotheses decomposition strategy is employed to reduce the dimensions of association process, so that partitioning of the detections set is avoided. Experiments indicate that the proposed method can estimate the target number without bias, and significantly outperforms the multi-detection probability hypothesis density(MD-PHD) filter in low probability of detection situation. The computational cost of the proposed method is higher than MD-PHD with a few detections, and grows slowly than MD-PHD when the amount of detections increases.