摘要
本论文深入探究基于机器视觉与YOLOv5s的自动计数系统中的物料检测部分。首先介绍YOLOv5相关背景,YOLOv5具有卓越性能优势,在速度与精度间取得良好平衡,其在目标检测领域的研究持续深入且应用广泛。随后着重对YOLOv5s网络模型展开剖析,其网络模型由多个关键部分构成。Input输入端通过独特的数据增强策略扩充数据量与丰富度;Backbone主干网络运用多种创新模块高效提取深度特征;neck网络层进行特征融合与传递,整合多尺度信息;head输出端最终生成精准的检测结果。通过全面解析该网络模型,为理解其在物料检测中的应用机制奠定基础,进而深入挖掘基于此的自动计数系统在物料检测方面的性能优势,推动相关技术在工业等领域的应用拓展。
This thesis conducts an in-depth exploration of the material detection part in the automatic counting system based on machine vision and YOLOv5s.Firstly,the relevant background of YOLOv5 is introduced.YOLOv5 has outstanding performance advantages,achieving a good balance between speed and accuracy.Its research in the field of object detection has been continuously deepened and widely applied.Subsequently,the focus is on the analysis of the YOLOv5s network model,which consists of several key components.The Input end expands the data volume and enriches its diversity through unique data augmentation strategies;the Backbone main network efficiently extracts deep features by using various innovative modules;the neck network layer performs feature fusion and transmission,integrating multi-scale information;and the head output end finally generates accurate detection results.Through a comprehensive analysis of this network model,it lays the foundation for understanding its application mechanism in material detection,further explores the performance advantages of the automatic counting system based on it in material detection,and promotes the application expansion of related technologies in industries and other fields.
作者
米宏维
赵洋
MI Hong-wei;ZHAO Yang(Huanghe Xinye Co.,Ltd.,Xining,Qinghai 810000)
出处
《世界有色金属》
2025年第8期30-33,共4页
World Nonferrous Metals
关键词
物料检测
网络模型
Material Detection
Network Model
