Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (1): 92-99.DOI: 10.3778/j.issn.1002-8331.2206-0231

• Improvement and Application of YOLO • Previous Articles     Next Articles

Lightweight Research of YOLOv5 Target Detection

HE Yu, TIAN Junwei, ZHANG Zhen, WANG Qin, ZHAO Peng   

  1. School of Mechanical and Electrical Engineering, Xi’an University of Technology, Xi'an 710021, China
  • Online:2023-01-01 Published:2023-01-01



  1. 西安工业大学 机电工程学院,西安 710021

Abstract: The existing target detection algorithms usually have problems such as large size and complex structure, which leads to poor recognition speed and accuracy in the process of indoor robot operation. To solve this problem, based on indoor target detection, a lightweight detection method of improved YOLOv5s is proposed. This method mainly introduces the ShuffleNet v2 feature extraction mechanism based on the YOLOv5s network to realize the lightweight of the network. At the same time, the weighted bidirectional feature pyramid BiFPN and the frame regression loss EIOU are used to obtain the feature map with richer feature information to improve the accuracy of target detection, so as to obtain a new indoor target detection model. The experimental results show that the parameters of the improved model are significantly reduced, the complexity of the model is reduced by 46%, and the average accuracy rate is increased to 63.9%. The balance between lightweight and detection accuracy is achieved, which provides a reference for target lightweight research.

Key words: target detection, YOLOv5s, ShuffleNetv2, lightweight, frame regression loss EIOU

摘要: 现有目标检测算法通常存在体积较大、结构复杂等问题,致使室内机器人作业过程中识别速率与精度较差。针对这一问题,以室内目标检测为基础,提出了一种改进的YOLOv5s轻量化检测方法。该方法主要是在YOLOv5s网络的基础上引入ShuffleNet v2特征提取机制来实现网络的轻量化,同时采用加权双向特征金字塔BiFPN和边框回归损失EIOU获取特征信息更为丰富的特征图,来提升目标检测精度,从而得到一种新的室内目标检测模型。研究结果表明,改进后的模型参数量明显减少,模型复杂度减少了46%,平均精确率均值mAP提升到63.9%,实现了轻量化和检测准确率的平衡,该研究为目标轻量化研究提供了参考。

关键词: 目标检测, YOLOv5s, ShuffleNetv2, 轻量化, 边框回归损失EIOU