Classification subnetwork and box regression subnetwork are essential components in deep networks for object detection. However, we observe a contradiction that before NMS, some better localized detections do not correspond to higher classification confidences, and vice versa. This contradiction exists because classification confidences can not fully reflect the localization-quality (loc-quality) of each detection. In this work, we propose the Localization-quality Estimation embedded Detector abbreviated as LED, and a corresponding detection pipeline. In this detection pipeline, we first propose an accurate loc-quality estimation method for each detection, then combine the loc-quality with the corresponding classification confidence during inference to make each detection more reasonable and accurate. For efficiency, LED is designed as an one-stage network. Extensive experiments are conducted on Pascal VOC 2007 and KITTI car detection datasets to demonstrate the effectiveness of LED.