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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.
Yuan Xiao-ping,马绪起,刘赛.An Improved Algorithm of Pedestrian and Vehicle Detection Based on YOLOv3[J].Science Technology and Engineering,2021,21(8):3192-3198.
改进YOLOv3的行人车辆目标检测算法
An Improved Algorithm of Pedestrian and Vehicle Detection Based on YOLOv3
投稿时间:2020-07-03  修订日期:2020-12-18
DOI:
中文关键词:  深度学习  目标检测  YOLOv3  ResneXt  Densenet
英文关键词:deep learning  object detection  YOLOv3  ResneXt  Densenet
基金项目:科技部科技支撑项目( 2013BAK06B08)
        
作者单位
袁小平 中国矿业大学
马绪起 中国矿业大学
刘赛 中国矿业大学
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中文摘要:
      针对YOLOv3对中小目标检测效果不理想的问题,提出改进算法DX-YOLO。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;受DenseNet的启发,在Darknet-53中引入密集连接,实现了特征重用,提高了提取特征的效率;根据数据集的特点,利用K-means算法对数据集进行维度聚类,获得合适的预选框。在行人车辆数据集Udacity上进行实验,结果表明DX-YOLO算法与YOLOv3相比,mAP提升了3.42%;特别地,在中等目标和小目标上的AP值分别提升了2.74%和5.98%。
英文摘要:
      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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