Improvement of Pedestrian Detection Algorithm Based on YOLO

摘要：

　　The non-maximum suppression algorithm is usually used in the post-position of deep learning object detection algorithm.It suppresses the detection boxes with high overlap rate while using the algorithm.In order to avoid the missed and false detection caused by non-maximum suppression algorithm,an improved maximum value suppression algorithm is proposed.When the IOU of the suppression window and the suppressed window is greater than the given threshold,the confidence multiply by the penalty factor instead of discarding it directly.After multiple iterations,we need to remove the lower scores detection boxes.Experiments show that the YOLO V2 deep learning model with improved algorithm has improved accuracy on different data sets as well as strong versatility and robustness.

关键词： deep learning object detection detection boxes penalty factor YOLO V2

作者: Xuan Li Jing Li

作者单位: School of Electronic Information Engineering,Shenyang Aerospace University,Shenyang 110136,China

会议类型: 国际会议

会议名称: 2019 2nd International Conference on Mechanical Engineering, Industrial Materials and Industrial Electronics (MEIMIE 2019)2019年第二届机械工程、工业材料和工业电子国际会议(Meimie 2019)

会议地点: 大连

会议语种:英文

页码: 44-49

在线出版日期: 2019-03-29（万方平台首次上网日期，不代表论文的发表时间）