计算机工程与应用2024，Vol.60Issue(14)：187-196,10.DOI:10.3778/j.issn.1002-8331.2312-0394
面向工业表面缺陷检测的改进YOLOv8算法
Improved YOLOv8 Algorithm for Industrial Surface Defect Detection
摘要
Abstract
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 scenar-ios with limited resources.Two industrial surface defect datasets,GC10-DET and DeepPCB,are used to validate the effec-tiveness of the improved EML-YOLO algorithm.The experimental results show that on the GC10-DET dataset and Deep-PCB 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 detec-tion scenarios.关键词
缺陷检测/高效大卷积模块/多尺度特征/特征压缩/YOLOv8Key words
defect detection/efficient large convolution module/multi-scale features/feature compression/YOLOv8分类
信息技术与安全科学引用本文复制引用
苏佳,贾泽,秦一畅,张建燕..面向工业表面缺陷检测的改进YOLOv8算法[J].计算机工程与应用,2024,60(14):187-196,10.基金项目
国家自然科学基金青年科学基金(62105093). （62105093）
