[1]鲍先富,强赞霞,李丹阳,等.基于组卷积特征融合的 One-Stage 目标检测模型[J].计算机技术与发展,2021,31(11):86-94.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 015]
　BAO Xian-fu,QIANG Zan-xia,LI Dan-yang,et al.One-Stage Target Detection Model Based on Group ConvolutionFeature Fusion[J].,2021,31(11):86-94.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 015]
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基于组卷积特征融合的 One-Stage 目标检测模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年11期
页码:
86-94
栏目:
图形与图像
出版日期:
2021-11-10

文章信息/Info

Title:
One-Stage Target Detection Model Based on Group ConvolutionFeature Fusion
文章编号:
1673-629X(2021)11-0086-09
作者:
鲍先富; 强赞霞; 李丹阳; 杨 瑞
中原工学院,河南 郑州 450007
Author(s):
BAO Xian-fu; QIANG Zan-xia; LI Dan-yang; YANG Rui
Zhongyuan University of Technology,Zhengzhou 450007,China
关键词:
卷积神经网络; 目标检测; 残差网络; 特征融合金字塔; 通道洗牌; 组卷积
Keywords:
convolutional neural network; target detection; residual network; feature fusion pyramid; channel shuffle; group convolution
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 11. 015
摘要:
由于移动终端计算能力和内存大小的限制, 在移动设备上进行实时目标检测具有非常大的挑战性。 为了更好地在无人驾驶汽车等移动设备上进行目标检测, 该文以 YOLOv3 单阶段目标检测模型为基础,对部署在移动设备上的目标检测模型进行优化, 以提高模型的检测精度( MAP)并降低计算复杂度。 具体改进措施为:基于 DarkNet-53 为主干网络引入组卷积和通道洗牌技术;基于 M. G. Hluchyj 等学者提出的网络设计指导原则,对主干网络的残差单元和下采样单元进行修改优化;为减轻 YOLOv3 模型对于密集目标的漏选和标签重写问题,引入特征混合金字塔模型。 通过在 Pascal VOC2007和 VOC2012 数据集上进行实验对比,优化模型的整体精度较 YOLOv3 提高 8. 17% ,模型参数量降低 1. 21 M,在与 YOLOv4的参数量大体相等的情况下达到了 YOLOv4 的检测精度。
Abstract:
Due to the limitations of mobile terminal computing power and memory size,real-time target detection on mobile devices is quite challenging.? ? ? ?In order to better perform target detection on mobile devices such as driver less cars,based on YOLOv3 single-stage target detection model,? ? ?the target detection model deployed on the mobile device is optimized to improve the detection accuracy ( MAP)and reduce the computational complexity. The specific improvement measures are as follows:the introduction of volume and channel shuffling technology based on the DarkNet-53 backbone network; based on the network design guidelines proposed by scholars such as MG Hluchyj,the residual unit and down-sampling unit of the backbone network are modified and optimized; the feature mixture pyramid model is introduced to reduce the YOLOv3 model’ s omission of dense targets and label rewriting. Through experimental comparison on the Pascal VOC2007 and VOC2012 data sets,the overall accuracy of the optimized model is 8. 17% higher than that of YOLOv3,and the model parameter is reduced by 1. 21 M. The detection accuracy? of YOLOv4 is reached when the parameter quantity of YOLOv4 is roughly equal.

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更新日期/Last Update: 2021-11-10