Existing label assignment strategies have achieved promising performance for providing learning targets, typically rely on statistical information and location characteristics. However, classical label assignment strategies usually lack a comprehensive quality evaluation criterion. As a result, the quality of positive samples is not reliable, which weakens the performance of detectors. We propose a dynamic label assignment strategy to provide higher quality positive samples for detection models. Specifically, we propose a quality assessment criteria of candidate samples, which uses a joint representation guided by intersection over union (IoU). Consequently, the supervised information of both branches is included using only one metric. Beside, we propose a partitioning approach to eliminate local redundant sampling, allowing the selected positive sample points to focus more on the overall information of the target. Tests on the COCO dataset show that our work improves the baseline by 2.2% AP without additional modeling and supervisory information. In addition, extensive experiments on the MS COCO test-dev dataset using different backbones demonstrate that our best model outperforms most of the existing representative methods.