Improved YOLOv8 Algorithm for Industrial Surface Defect Detection

被引：0

作者：

Su, Jia ^{[1
]}

Jia, Ze ^{[1
]}

Qin, Yichang ^{[1
]}

Zhang, Jianyan ^{[1
]}

机构：

[1] School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang,050018, China

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 14期

关键词：

Convolution;

D O I：

10.3778/j.issn.1002-8331.2312-0394

中图分类号：

学科分类号：

摘要：

Aiming at the problems of low contrast of industrial defects and high false detection rate and leakage rate caused by the surrounding interference information, it proposes an industrial surface defect detection algorithm EML-YOLO based on the improvement of YOLOv8. By designing a high-efficiency large convolution module ELK, the model’ s feature extraction capability can be improved by providing a multi-scale feature representation while retaining the spatial information; by proposing a parallel multi-branch feature fusion module MCM, which enables the model to acquire rich feature information and global context information; and reducing the number of parameters and computation of the model by feature compression and streamlining in the Neck module, which makes the model more applicable to industrial scenarios with limited resources. Two industrial surface defect datasets, GC10-DET and DeepPCB, are used to validate the effectiveness of the improved EML-YOLO algorithm. The experimental results show that on the GC10-DET dataset and DeepPCB dataset, the detection accuracy is improved by 4.3 percentage points and 2.9 percentage points, respectively, and the number of parametric quantities is only 2.7×106. The proposed algorithm can be better applied to industrial defect detection scenarios. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：187 / 196