Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (3): 109-118.DOI: 10.3778/j.issn.1002-8331.2305-0109

• Special Issue on Object Detection • Previous Articles     Next Articles

Research on Lightweight Improved Algorithm for Indoor Target Detection Based on YOLOv5s

NIU Xinyu, MAO Pengjun, DUAN Yuntao, LOU Xiaoheng   

  1. School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, China
  • Online:2024-02-01 Published:2024-02-01



  1. 河南科技大学 机电工程学院,河南 洛阳 471003

Abstract: The existing indoor target detection algorithms have many problems, such as complex structure, large amount of calculation and large number of model parameters, which are difficult to be deployed to the indoor robot platform with limited computing capacity to achieve efficient target detection. To solve this problem, an improved YOLOV5s detection algorithm is proposed. In this method, ShuffleNetv2 is introduced as the backbone feature extraction network, and CA attention mechanism is adopted on the basis of the improved backbone network, and GSConv and VOV-GSCSP modules are adopted in the neck network. Finally, the bounding regression loss function EIOU is introduced to accelerate the network convergence. The results show that the improved target detection algorithm reduces the model computation by 68.75%, the number of model parameters by 62.2%, the weight file by 59.7%, and the average accuracy mAP is 0.653. The improved target detection model can ensure the detection accuracy while ensuring the lightweight.

Key words: YOLOv5s, light weight, ShuffleNetv2, CA attention mechanism, GSConv module, VOV-GSCSP module, EIOU loss function

摘要: 针对现有室内目标检测算法,存在结构复杂,计算量以及模型参数量过大等问题,难以部署到计算能力有限的室内机器人平台,实现高效的目标检测。为解决这一问题,提出了一种改进的YOLOV5s轻量化检测算法。该方法采用ShuffleNetv2作为主干特征提取网络,并且在改进的主干网络基础上采用CA注意力机制,同时在颈部网络中采用GSConv和VOV-GSCSP模块。最后引入边框回归损失函数EIOU加快网络收敛。研究结果表明,改进后的目标检测算法,模型计算量减少了68.75%,模型参数量减少了62.2%,权重文件减少了59.7%,平均精确率mAP均值为0.653,改进后的目标检测模型能够在保证轻量化的同时保证检测精度。

关键词: YOLOv5s, 轻量化, ShuffleNetv2网络, CA注意力机制, GSConv模块, VOV-GSCSP模块, EIOU损失函数