基于改进YOLOv5s的仓储货物检测算法研究
Research on an Improved YOLOv5s-based Algorithm for Warehouse Goods Detection
王影 1王晨 1贾永涛 2刘麒1
作者信息
- 1. 吉林化工学院信息与控制工程学院,吉林吉林 132022
- 2. 中国石油吉林石化公司化肥厂,吉林吉林 132000
- 折叠
摘要
针对目前仓储货物分类速度慢、易出错、灵活性差等问题,提出了一种改进YOLOv5s的货物检测算法,对仓储货物进行预分类.首先,根据仓储货物的外形特征,将其分为包装箱与包装袋两大类,形成训练数据集;其次,将骨干网络更换为具有更小模型尺寸的MobileNetV3,加快推理;再次,添加SE注意力机制模块,旨在提高模型的检测精度;最后,结合α_CIoU损失函数,增强模型的灵活度.通过实验验证,改进后的算法相较于原始算法在精确率(Precision,P)、平均类别精度(mean Average precision,mAP)和帧率(Frames per second,FPS)三方面分别提升2.1％、0.5％和10.6％,能够高效地完成对仓储货物的预分类工作.
Abstract
A modified YOLOv5s detection algorithm had been proposed to address the issues of slow classification speed,error-proneness,and low flexibility in warehouse goods categorization.The algorithm aims to pre-classify warehouse goods.Firstly,based on the external characteristics of warehouse goods,they were divided into two main categories:packaging boxes and packaging bags,forming a training dataset.Secondly,the backbone network was replaced with MobileNetV3,a smaller-sized model,to accelerate inference.Additionally,an SE attention mechanism module was added to enhance the detection accuracy of the model.Finally,the α_CIoU loss function was incorporated to improve the flexibility of the model.Experimental results demonstrated that the improved algorithm achieves a 2.1％increase in precision(P),a 0.5％increase in mean Average Precision(mAP),and a 10.6％increase in Frames Per Second(FPS)compared to the original algorithm.It enables efficient pre-classification of warehouse goods.
关键词
YOLOv5s/仓储货物/检测算法/预分类Key words
YOLOv5s/warehouse goods/detection algorithm/pre-classification引用本文复制引用
基金项目
吉林市科技成果项目(201750244)
吉林市科技成果项目(20190502118)
吉林化工学院科学研究项目(2018064)
吉林化工学院重大科技项目(2016033)
吉林化工学院重大科技项目(2018017)
出版年
2024
NETL
NSTL
万方数据