自动化与信息工程2024，Vol.45Issue(2)：34-40,7.DOI:10.3969/j.issn.1674-2605.2024.02.006
基于MBE-YOLOv5的轻量化化工袋目标检测方法
Lightweight Chemical Bag Target Detection Method Based on MBE-YOLOv5
刘伟鑫 1林邦演 1黄汉亿 1李旻龙1
作者信息
- 1. 东莞市新一代人工智能产业技术研究院,广东 东莞 523867
- 折叠
摘要
Abstract
A lightweight chemical bag target detection method based on MBE-YOLOv5 is proposed to address the issues of poor positioning and real-time performance of chemical bag recognition models in chemical plants,which are caused by various types of chemical bags,occlusion interference,and complex placement.Firstly,replace the backbone network of YOLOv5 with MobileNetV3 network to reduce model parameters and computational complexity,and improve the detection speed of the model;Then,a bidirectional feature pyramid network structure is introduced into the neck network of YOLOv5 for multi-scale feature fusion to improve the recognition accuracy of the model;Finally,the EIoU function is used to optimize the loss and improve the positioning accuracy of the model.The experimental results show that compared to the YOLOv5 model,the MBE-YOLOv5 model reduces the number of parameters by 37.7%,the computational complexity by 58.1%,and the detection speed by 9.5%,mAP@0.5 Improved by 0.7%;Achieving a good balance between detection speed and accuracy can meet the requirements of online detection,recognition,and positioning of chemical bags.关键词
YOLOv5模型/MobileNetV3网络/双向特征金字塔网络/EIoU函数/化工袋目标检测Key words
YOLOv5 model/MobileNetV3 network/bidirectional feature pyramid network/EIoU function/chemical bag target detection分类
信息技术与安全科学引用本文复制引用
刘伟鑫,林邦演,黄汉亿,李旻龙..基于MBE-YOLOv5的轻量化化工袋目标检测方法[J].自动化与信息工程,2024,45(2):34-40,7.
