期刊论文

Application of Improved YOLOv7 Algorithm in Detection of Capping Defects of Vials

中文

计算机与现代化

1006-2475

2023

12

82-86

10.3969/j.issn.1006-2475.2023.12.014

国家自然科学基金资助项目(42074198)。

本校为第一机构

针对西林瓶轧盖缺陷检测中存在目标缺陷较小和特征不清造成的漏检问题,提出一种基于改进YOLOv7算法的缺陷检测方法。首先在真实工业环境下采集西林瓶轧盖的缺陷图像,包括划痕、缺帽、凹裂、复合缺陷4种常见缺陷,并进行数据增强,...展开更多 针对西林瓶轧盖缺陷检测中存在目标缺陷较小和特征不清造成的漏检问题,提出一种基于改进YOLOv7算法的缺陷检测方法。首先在真实工业环境下采集西林瓶轧盖的缺陷图像,包括划痕、缺帽、凹裂、复合缺陷4种常见缺陷,并进行数据增强,构造一个具有3220张西林瓶轧盖缺陷图像的数据集。然后在原始YOLOv7的基础上引入CBAM(Convolution Block Attention Module)注意力...

Aiming at the problem of missing inspection caused by small target defects and unclear features in the defect detection of the capping of penicillin bottles,this paper proposes a defect detection method based on the improved YOLOv7 algorithm.First,the defect images of t...MORE Aiming at the problem of missing inspection caused by small target defects and unclear features in the defect detection of the capping of penicillin bottles,this paper proposes a defect detection method based on the improved YOLOv7 algorithm.First,the defect images of the capping of penicillin bottles are collected in th...