摘要
针对YOLOv3算法实时目标检测性能不佳的缺陷,提出了一种适应实时目标检测的改进网络结构以及视频目标检测的新方法。首先,提出的k-means-threshold(k-thresh)方法弥补了k-means算法对聚类中心初始位置十分敏感的问题,在包括三个类别的数据集中进行聚类分析选择合适的锚框;然后,将4倍下采样和8倍下采样特征图拼接融入第三个检测层,以提高对目标的检测精度,将YOLOv3算法的平均准确率均值提高了2%;最后,通过摄像头捕捉图像和前期得到的优秀检测数据来预测新图像的目标以及加入了重新检测阈值,以提高视频检测流畅度。实验结果表明:所提基于改进的YOLOv3网络在检测精度上得以提高,实时性也有所提高,在30 min的实时检测中最大帧率达到64.26 frame/s,相比原始YOLOv3算法,实时检测速度提高了4倍左右。
For the shortcoming of the real-time performance of YOLOv3 algorithm in object detection,we propose an improved network structure and a new method for video object detection adapted to real-time object detection.Firstly,the proposed k-means-threshold(k-thresh)method makes up for the problem of its sensitivities to the initial position of the cluster center,and performs cluster analysis on a data set including three categories to select more appropriate anchor boxes.Then,the 4×down-sampling and 8×down-sampling feature maps are stitched together into the third layer detection layer to improve the detection accuracy of the object and increase the the mean average precision of the YOLOv3 algorithm by 2%.Finally,the camera captures the image and the excellent detection data obtained in the previous period to predict the target of the new image and adds a re-detection threshold to improve the smoothness of video detection.The experimental results show that the proposed improved YOLOv3 network improves the detection accuracy and the real-time performance,the maximum frame rate reaches 64.26 frame/s in 30 min of real-time detection,which is 4 times faster than the original YOLOv3 algorithm.
作者
孙佳
郭大波
杨甜甜
马识途
Jia Sun;Dabo Guo;Tiantian Yang;Shitu Ma(College of Physics and Electronic Engineering,Shanxi University,Taiyuan,Shanxi 030006,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第22期323-332,共10页
Laser & Optoelectronics Progress
基金
山西省基础研究项目(201801D121118)。
