基于改进Mask R-CNN算法的工业零件缺陷检测技术  

Industrial Parts Defect Detection Technology Based on Improved Mask R-CNN Algorithm

作　　者：尚洁 吴观茂[1] SHANG Jie;WU Guanmao(College of Computer Science and Engineering,Anhui University of Science&Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学计算机科学与工程学院,安徽淮南232001

出　　处：《现代信息科技》2022年第3期137-140,共4页Modern Information Technology

基　　金：安徽省自然科学基金面上项目(1908085MF189)。

摘　　要：为了提升工业零件缺陷检测的精度和速度,在Mask R-CNN的基础上,引入了引导锚框的anchor生成方案提升检测精度;在此基础上对Mask R-CNN网络框架进行改进,去掉Mask分支,实现检测速度的优化。采用的数据集是DAGM工业缺陷数据集,并与先前的代表方法进行对比实验。实验表明,改进后的算法在检测精度方面对比原始算法提升了约1.94%,且速度也提升了1.2 frame/s,提升了工业零件缺陷检测的速度和精度。In order to improve the accuracy and speed of industrial parts defect detection,on the basis of Mask R-CNN,the anchor generation scheme of the guide anchor frame is introduced to improve the detection accuracy.on this basis,the Mask R-CNN network framework is improved and Mask branch is removed to realize the optimization of detection speed.The data set used is the DAGM industrial defect data set,and a comparison experiment with the previous representative method is carried out.Experiments show that the improved algorithm has improved by 1.94%in detection accuracy compared with the original algorithm,and the speed has also improved by 1.2 frame/s,the speed and accuracy of industrial parts defect detection are improved.

关 键 词：工业缺陷检测 Mask R-CNN 引导锚框 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]