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。
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.
作者
金瑶
张锐
尹东
Jin Yao;Zhang Rui;Yin Dong(College of Information Science and Technology,University of Science and Technology of China,Hefei,Anhui 230027,China;Key Laboratory of Electromagnetic Space Information,Chinese Academy of Sciences,Hefei,Anhui 230027,China)
出处
《光电工程》
CAS
CSCD
北大核心
2019年第9期74-81,共8页
Opto-Electronic Engineering
基金
2018年度安徽省重点研究和开发计划项目(1804a09020049)~~
关键词
视频图像
小像素目标
卷积神经网络
video image
smaller pixel object
convolutional neural network
