YOLO-wLU：考虑定位不确定性的目标检测算法

YOLOv3目标检测算法在检测目标时没有考虑边界框坐标定位存在的不确定性，因此有时不能得出正确的检测结果。针对此问题，提出YOLO-wLU（YOLO with Localization Uncertainty）算法。该算法借鉴深度学习中的不确定性思想，使用高斯分布函数建立边界框坐标的概率分布模型以考虑边界框坐标定位不确定性；设计新的边界框损失函数，在检测过程中移除定位不确定性较大的检测结果；通过融合周围边界框坐标信息提高了边界框坐标辨识结果的准确性。实验结果表明，该算法可有效减少误报率，提高检测精度；COCO数据集上测试结果显示，相比YOLOv3算法，该算法的mAP最高可提升4.1个百分点。

YOLO-wLU:Object Detection Algorithm Considering Localization Uncertainty

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.