The standard object detection algorithm, YOLOv3, does not consider the bounding box coordinates localization uncertainty. Consequently, it will yield incorrect detection results sometimes. To solve this problem, the YOLO-wLU algorithm（YOLO with Localization Uncertainty） based on YOLOv3 is proposed. By the idea of uncertainty in the deep learning, the Gaussian distribution function is used for modeling the bounding box coordinates probability distribution to consider the bounding box coordinates localization uncertainty. Moreover, a new bounding box loss function is designed, and the detection results with larger localization uncertainty is removed. Meanwhile, the accuracy of bounding box coordinates identification results is improved by merging the surrounding bounding boxes coordinates information. The experimental results show that the proposed YOLO-wLU algorithm effectively reduces the FP（False Positive） rate, and improves the detection precision; more concretely, the proposed YOLO-wLU algorithm improves mAP at most 4.1 percentage points compared with the YOLOv3 algorithm on the COCO test dataset.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2007-0234