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改进的YOLO V3算法及其在小目标检测中的应用
Alternative TitleImproved YOLO V3 algorithm and its application in small target detection
鞠默然1,2,3,4,5; 罗海波1,2,4,5; 王仲博1,2,3,4,5; 何淼1,2,3,4,5; 常铮1,2,4,5; 惠斌1,2,4,5
Source Publication光学学报
Indexed ByEI ; CSCD
EI Accession number20193607400976
Contribution Rank1
Keyword机器视觉 小目标检测 YOLO V3 VEDAI数据集 K-means

针对图像中小目标检测率低、虚警率高等问题,提出了一种YOLO V3的改进方法,并将其专用于小目标的检测。由于小目标所占的像素少,特征不明显,提出对原网络输出的8倍降采样特征图进行2倍的上采样,与第二个残差块(Res block)输出的特征图进行拼接,建立输出为4倍降采样的特征融合目标检测层。为了获取更多的小目标特征信息,在YOLO V3网络结构Darknet53的第二个残差块中增加2个残差单元(Resnet unit)。利用K-means聚类算法对目标候选框的个数和宽高比维度进行聚类分析。用改进的网络在VEDAI航拍车辆数据集上与YOLO V3进行对比试验,结果表明改进后的网络能有效检测小目标,对小目标的召回率和检测的平均准确率都有明显的提升。

Other Abstract

Aiming at the problems of low detection rate and high false alarm rate of the small targets in the image, an improved detection algorithm of YOLO V3 is proposed and specially applied in small target detection. Because the resolution of small targets is low and the features are not obvious, it is proposed to upsample by 2× the feature map which is downsampled by 8× of the previous network, and concatenate it with the output of the second Res block unit. The feature fusion target detection layer whose feature map is downsampled by 4× is established. To obtain more features of the small target, add two Resnet units in the second Res block unit of Darknet53 in YOLO V3 network structure. K-means clustering algorithm is used to select the number of the candidate anchor boxes and the aspect ratio dimensions. The comparative experiment on VEDAI dataset with YOLO V3 shows that the improved network can detect small targets efficiently and improve the accuracy rate and the recall rate of the small targets.

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Document Type期刊论文
Corresponding Author鞠默然
Recommended Citation
GB/T 7714
鞠默然,罗海波,王仲博,等. 改进的YOLO V3算法及其在小目标检测中的应用[J]. 光学学报,2019,39(7):1-8.
APA 鞠默然,罗海波,王仲博,何淼,常铮,&惠斌.(2019).改进的YOLO V3算法及其在小目标检测中的应用.光学学报,39(7),1-8.
MLA 鞠默然,et al."改进的YOLO V3算法及其在小目标检测中的应用".光学学报 39.7(2019):1-8.
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