Non-Maximum Suppression （NMS） is a post-processing algorithm used for object detection. It selects optimal bounding boxes from the bounding boxes set and suppresses other bounding boxes. NMS selects the bounding box with the highest score of classification confidence as the optimal bounding box. However， it ignores the correlation between localization accuracy and the classification confidence score. The classification confidence score cannot effectively represent the localization accuracy. This paper proposes a novel Relocation Non-Maximum Suppression （R-NMS） algorithm to solve the above-mentioned problem. First， the bounding box with the highest score of classification confidence in the bounding boxes set is selected as the optimal bounding box. Second， a new box distance measurement method is proposed based on R-NMS instead of using Intersection over Union （IoU） to measure the distance between the bounding boxes. Then， the location information of the bounding boxes around the optimal bounding box is obtained. Finally， the location information is used to relocate the optimal bounding box to obtain the new optimal bounding box. Compared with NMS and Soft-NMS， the mAP of R-NMS on YOLOv3 increased by 0.7 % and 0.5 %， respectively. The mAP of R-NMS on Faster-RCNN is 80.83 %， and the effectiveness of the proposed algorithm in the improvement of the mAP of various object detectors is confirmed.