相关实验视频

Updated: Jul 1, 2025

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

Published on: February 3, 2018

基于YOLOv5的物体检测在卷轴包中半导体组件的X射线图像

Jinwoo Park^1,2, Jaehyeong Lee¹, Jongpil Jeong¹

¹Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.

|March 4, 2024

概括

改进的YOLOv5模型增强了人工智能 (AI) 深度学习,用于制造业中检测小物体. 这种人工智能驱动的方法可以提高复杂元件的实时质量控制的准确性和效率.

科学领域:

制造业制造技术制造技术
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

工业制造业越来越多地采用人工智能 (AI) 来实现智能工厂基础设施.
人工智能驱动的战略对于提高生产和质量控制流程至关重要.
人工智能驱动的计算机视觉比传统的基于规则的系统提供了显著的改进.

研究的目的:

为AI深度学习促进小物体检测提供YOLOv5模型的优化代.
提高工业环境中对象检测的准确性和计算效率.
为满足制造业中微型组件的精确实时评估需求.

主要方法:

通过向其标准的三层架构添加额外的一层,开发了增强的YOLOv5模型.
该模型利用PyTorch进行单阶段物体检测功能.
使用半导体X射线图像进行实证评估,以评估性能.

主要成果:

改进的YOLOv5模型实现了0.622的平均平均精度 (mAP),明显超过了原始YOLOv5的0.349.
精度提高到0.865,比YOLOv5的0.552.5大大提高了精度.
该模型在高精度检测小物体方面表现出卓越的性能.

结论:

优化的YOLOv5模型为制造业的实时,在线生产评估提供了强大的解决方案.
它的提高准确性和效率对于检测表面安装技术中的微尺度组件尤其有益.
该模型显示了各种工业应用的巨大潜力,需要颗粒物体检测.

关键词:

人工智能的人工智能是人工智能.一个半导体半导体.小物体检测小物体检测这是X射线.这是YOLOv5的.

更多相关视频

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography CT and Light Microscopy LM Correlated with Scanning Electron Microscopy SEM

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography CT and Light Microscopy LM Correlated with Scanning Electron Microscopy SEM

Published on: June 16, 2016

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

相关概念视频

Semiconductors

Semiconductors

There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...