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城市道路视频中小像素目标检测

Object detection for small pixel in urban roads videos

金瑶   张锐   尹东  
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摘要

视频图像中的小像素目标难以检测。针对城市道路视频中的小像素目标,本文提出了一种改进YOLOv3 的卷积神经网络Road_Net 检测方法。首先,基于改进的YOLOv3,设计了一种新的卷积神经网络Road_Net;其次,针对小像素目标检测更依赖于浅层特征,采用了4 个尺度检测方法。最后,结合改进的M-Softer-NMS 算法来进一步提高图像中目标的检测精度。为了验证所提出算法的有效性,本文收集并标注了用于城市道路小像素目标物体检测的数据集Road-garbage Dataset,实验结果表明,本文算法能有效地检测出诸如纸屑、石块等在视频中相对于路面的较小像素目标。

Abstract

Small pixel targets in video images are difficult to detect. Aiming at the small pixel target in urban road video, this paper proposed a novel detection method named Road_Net based on the YOLOv3 convolutional neural network. Firstly, based on the improved YOLOv3, a new convolutional neural network Road_Net is designed. Secondly, for small pixel target detection depending on shallow level features, a detection method of 4 scales is adopted. Finally, combined with the improved M-Softer-NMS algorithm, it gets higher detection accuracy of the target in the image. In order to verify the effectiveness of the proposed algorithm, this paper collects and labels the data set named Road-garbage Dataset for small pixel target object detection on urban roads. The experimental results show that the algorithm can effectively detect objects such as paper scraps and stones, which are smaller pixel targets in the video relative to the road surface.

Newport宣传-MKS新实验室计划
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中图分类号:TB872;TP391.4

DOI:10.12086/oee.2019.190053

所属栏目:科研论文

基金项目:2018 年度安徽省重点研究和开发计划项目(1804a09020049)

收稿日期:2019-01-30

修改稿日期:2019-04-08

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作者单位    点击查看

金瑶:中国科学技术大学信息科学技术学院,安徽 合肥 230027中国科学院电磁空间信息重点实验室,安徽 合肥 230027
张锐:中国科学技术大学信息科学技术学院,安徽 合肥 230027中国科学院电磁空间信息重点实验室,安徽 合肥 230027
尹东:中国科学技术大学信息科学技术学院,安徽 合肥 230027中国科学院电磁空间信息重点实验室,安徽 合肥 230027

联系人作者:尹东(yindong@ustc.edu.cn)

备注:金瑶(1995-),女,硕士研究生,主要从事计算机视觉的研究。E-mail:joye@mail.ustc.edu.cn

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引用该论文

Jin Yao,Zhang Rui,Yin Dong. Object detection for small pixel in urban roads videos[J]. Opto-Electronic Engineering, 2019, 46(9): 190053

金瑶,张锐,尹东. 城市道路视频中小像素目标检测[J]. 光电工程, 2019, 46(9): 190053

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