Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (14): 166-175.DOI: 10.3778/j.issn.1002-8331.2302-0115

• Graphics and Image Processing • Previous Articles     Next Articles

Improved YOLOv7-tiny’s Object Detection Lightweight Model

LIU Haohan, FAN Yiming, HE Huaiqing, HUI Kanghua   

  1. School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
  • Online:2023-07-15 Published:2023-07-15



  1. 中国民航大学 计算机科学与技术学院,天津 300300

Abstract: At present, the object detection algorithm has a large number of parameters and high computational complexity. However, the storage capacity and computing power of mobile terminals are limited and it is difficult to deploy it. So in this paper, it proposes the improved YOLOv7-tiny for mobile terminal devices. An efficient backbone network and a lightweight feature fusion network are further proposed with the ShuffleNet v1-improved and EALN-GS as the basic building units. The combination of the two part can reduce computational complexity, obtain more rich semantic information, and further improve detection accuracy. The Mish activation function is used to increase nonlinear expression and improve the generalization ability of the model. Experimental results show that compared with the original model, the accuracy of the improved model is improved by 3.3%, the number of parameters and calculations are reduced by 4.8% and 13.7%, and the model scale is reduced by 8.7%. The improved YOLOv7-tiny reduces the amount of parameters and calculations of the model while maintaining high accuracy, further improves the detection effect, and provides feasibility for deployment in edge terminal devices.

Key words: object detection, YOLOv7-tiny, ShuffleNet v1, lightweight, Mish activation function, GSConv module

摘要: 当前目标检测算法参数量大、计算复杂度高,难以部署在计算资源有限的边缘终端设备上,为此,提出一种改进的YOLOv7-tiny模型。引入ShuffleNet v1网络,改进后作为新的特征提取网络,增强对图像特征的提取,降低计算复杂度,获取更多丰富的语义信息,进一步提升检测精度;引入GSConv(鬼影混洗卷积)模块改进网络的Neck层,在降低参数量和计算量前提下,提升检测效果;采用Mish激活函数,增加非线性表达,提高模型的泛化能力。实验结果表明,改进后的模型与原模型相比,精度提高了3.3%,参数量和计算量分别下降了4.8%和13.7%,模型规模降低了8.7%。改进后的YOLOv7-tiny在保持较高的精度下,降低了模型的参数量和计算量,进一步提升了检测效果,为在边缘终端设备部署提供了可行性。

关键词: 目标检测, YOLOv7-tiny, ShuffleNet v1, 轻量化, Mish激活函数, GSConv模块