首页|一种改进YOLOv5积木小零件检测算法研究

一种改进YOLOv5积木小零件检测算法研究

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针对积木零件种类繁多、人工分拣效率低等问题,提出一种基于改进YOLOv5 积木小零件算法检测系统.该算法使用双层Mosaic-16 进行数据增强,利用RGB矩阵完成对比度调整,通过对数据集的优化,实现对YOLOv5 算法的改进.实验结果表明:改进后的YOLOv5 算法能快速准确地对积木小零件进行识别分类,相比原始YOLOv5 算法,模型的训练速率和准确率大大提高.
Research on an Improved YOLOv5 Algorithm for Detecting Small Parts of Building Blocks
Targeting at the problems of various kinds of building block parts and low manual sorting efficiency,a small part of building block detection algorithm based on improved YOLOv5 is proposed.The algorithm uses double-layer Mosaic-16 for data enhancement and RGB matrix for contrast adjustment.Through the optimization of the dataset,the YOLOv5 algorithm is improved.The experimental re-sults show that the improved YOLOv5 algorithm can quickly and accurately identify and classify small building block parts.Compared with the original YOLOv5 algorithm,the training speed and accuracy of the model are greatly improved.

YOLOv5inspection of small building block partsMosaic-16 enhancements

徐微、郝琦琦、李波波、黄思绒

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西安交通大学城市学院电气与信息工程系,陕西 西安 710018

中煤陕西能源化工集团有限公司,陕西 榆林 719000

西安增材制造国家研究院有限公司,陕西 西安 710300

YOLOv5 积木小零件检测 Mosaic-16增强

陕西省教育科学"十三五"规划2020年度课题项目

SGH20Y1379

2024

电子器件
东南大学

电子器件

CSTPCD
影响因子:0.569
ISSN:1005-9490
年,卷(期):2024.47(4)