Non maximum suppression algorithm(NMS) is the main algorithm to select the accurate positioning box in object detection. The algorithm only takes the classification score as the standard, which may remove the prediction frame with low score but accurate positioning, and is more unfriendly to the situation with occlusion. A-NMS method is proposed, which integrates the attention mechanism into the non maximum suppression algorithm, and adjusts the final score of the box by combining the position information with the score information of the box. In addition, an improved distance based intersection union ratio loss function is proposed, the loss term is redefined, and it is introduced into non maximum suppression to calculate the intersection union ratio between frames instead of IOU. Finally, the two improved algorithms are integrated into three classical target detection. The above two algorithms are verified on Pascal VOC 2012 and MS-COCO 2017 data sets. The results show that the detection accuracy has been improved by 1% ~ 2%.