基于改进级联R-CNN的面料疵点检测方法
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2022-06-20 16:05
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文章编号 | 1009-265X(2022)02-0048-09
来源 | 《现代纺织技术》2022年第30卷,第2期,
作者 | 许胜宝1a,郑飂默2,3,袁德成1b
( 1.沈阳化工大学, a.计算机科学与技术学院; b.信息工程学院,沈阳 ;2.中国科学院沈阳计算技术研究所,沈阳 ;3.沈阳中科数控技术股份有限公司,沈阳 )
作者简介 | 许胜宝(1993-),男,辽宁丹东人,硕士研究生,主要从事计算机视觉方面的研究。
摘要 由于布匹疵点种类分布不均,部分疵点具有极端的宽高比,而且小目标较多,导致检测难度大,因此提出一种改进级联R-CNN的布匹疵点检测方法。针对小目标问题,在R-CNN部分采用在线难例挖掘,加强对小目标的训练;针对布匹疵点极端的长宽比,在特征提取网络中采用了可变形卷积v2来代替传统的正方形卷积,并结合布匹特征重新设计边界框比例。最后采用完全交并比损失作为边界框回归损失,获取更精确的目标边界框。结果表明:对比改进前的模型,改进后的模型预测边界框更加精确,对小目标的疵点检测效果更好,在准确率上提升了3.57%,平均精确度均值提升了6.45%,可以更好地满足面料疵点的检测需求。
关键词 级联R-CNN;面料疵点;检测;可变形卷积v2;在线难例挖掘;完全交并比损失
改进Cascade R-CNN的面料疵点检测方法
图1 Cascade R-CNN网络结构
Fig.1 Architecture of the Cascade R-CNN network
1.1 在线难例挖掘采样
图2 在线难例挖掘结构
Fig.2 Architecture of online hard example mining
1.2 可变形卷积v2
R={(-1,-1),(-1,0)...,(0,1),(1,1)}
(1)
(2)
(3)
(4)
图3 Resnet骨干网络结构
Fig.3 Architecture of Resnet backbone network
图4 可变形卷积示意
Fig.4 Diagram of deformable convolution
1.3 完全交并比损失函数
(5)
IoU Loss=-ln(IoU)
(6)
(7)
(8)
(9)
(10)
(11)
实验结果与对比分析
2.1 实验数据集
表1 布匹瑕疵的分类与数量
Tab.1 Classification and quantity of fabric defects
图5 不同类别目标数统计
Fig.5 Statistics of target number of different categories
图6 目标宽高比统计
Fig.6 Statistics of target aspect ratio
图7 典型疵点
Fig.7 Typical defects
图8 目标面积统计
Fig.8 Statistics of target area
2.2 实验环境及配置
2.3 实验结果对比
图9 模型改进前后的检测效果对比
Fig.9 The Comparison of detection effect before and after model improvement
表2 模型改进前后的评价参数
Tab.2 Evaluation parameter before and after model improvement %
表3 不同光照强度下测试集的对比
Tab.3 The comparison of the proposed algorithm under different light intensities on test sets
表4 引入OHEM前后的对比
Tab.4 The comparison before and after the introduction of OHEM
表5 算法在不同特征提取网络上的对比
Tab.5 The comparison of algorithms on different feature extraction networks
结 论
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