| 改进YOLOv3的行人车辆目标检测算法 |
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| 引用本文： | 袁小平,马绪起,刘赛. 改进YOLOv3的行人车辆目标检测算法[J]. 科学技术与工程, 2021, 21(8): 3192-3198. DOI: 10.3969/j.issn.1671-1815.2021.08.031 |
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| 作者姓名： | 袁小平 马绪起 刘赛 |
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| 作者单位： | 中国矿业大学信息与控制工程学院,徐州221116 |
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| 基金项目： | 科技部科技支撑项目( 2013BAK06B08) |
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| 摘 要： | 针对YOLOv3(you only look once version 3)对中小目标检测效果不理想的问题,提出改进算法DX-YOLO(densely ResneXt with YOLOv3).首先对YOLOv3的特征提取网络Darknet-53进行改进,使用ResneXt残差模块替换原有残差模块,优化了卷积网络结构;...
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| 关 键 词： | 深度学习 目标检测 YOLOv3 ResneXt DenseNet |
| 收稿时间： | 2020-07-03 |
| 修稿时间： | 2020-12-18 |
An Improved Algorithm of Pedestrian and Vehicle Detection Based on YOLOv3 |
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| Yuan Xiaoping,Ma Xuqi,Liu Sai. An Improved Algorithm of Pedestrian and Vehicle Detection Based on YOLOv3[J]. Science Technology and Engineering, 2021, 21(8): 3192-3198. DOI: 10.3969/j.issn.1671-1815.2021.08.031 |
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| Authors: | Yuan Xiaoping Ma Xuqi Liu Sai |
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| Affiliation: | China University of Mining and Technology |
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| Abstract: | Considering that YOLOv3 is not ideal for small and medium targets detection, an improved algorithm DX-YOLO is proposed. Firstly, the feature extraction network of YOLOv3 called Darknet-53 is improved, and the original residual module is replaced by ResneXt residual module, which optimizes the structure of convolution network. Inspired by Densenet, dense connection is introduced into Darknet-53 to realize feature reuse and improve the efficiency of feature extraction. According to the characteristics of data set, K-means algorithm is used to cluster the dimensions of data set to get the appropriate anchor box. Experiments on Udacity data set show that compared with YOLOv3, DX-YOLO algorithm improves the mAP by 3.42%; especially, the AP on medium and small targets increases by 2.74% and 5.98% respectively. |
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| Keywords: | deep learning object detection YOLOv3 ResneXt Densenet |
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