面向不均衡样本空间的工件表面缺陷检测方法

Surface Defect Detection Method of Workpiece for Unbalanced Sample Space

针对工件表面缺陷检测中样本的非均匀状态导致检测模型难以构建问题,提出了一种面向不均衡样本空间的工件表面缺陷检测方法。构建了包含样本空间均衡化采样模型(SSE Model)与缺陷检测模型(A-C Model)的串行整体结构(SSE-D Model)。SSE Model首先通过双路并行结构分别完成原始样本的特征提取与样本区域修复,随后对所提取特征利用单样本扩充实现特征的扩充,最后利用泊松融合实现特征与已修复样本的融合,生成新样本并完成样本空间的均衡化;A-C Model以空间均衡的新样本作为检测模型输入,利用深度残差思想构建检测模型,并融合注意力机制提升模型对缺陷特征的学习能力。该模型重点解决了工件表面缺陷检测中原始样本空间不均衡问题,并提升了检测模型对特征的学习能力与鲁棒性;最后利用5类工件图像样本完成实验对比,验证了本文方法的有效性与可行性,为不均衡样本空间的表面缺陷检测提供了一种新的思路。

Aiming at the problem that the inhomogeneous state of the sample in the detection of workpiece surface defects makes it to difficultly construct the detection model, a method of workpiece surface defect detection oriented to uneven sample space is proposed. A serial overall structure(SSE-D Model) including sample space equalization sampling model(SSE Model) and defect detection model(A-C Model) is constructed. SSE Model firstly uses a two-way parallel structure to perform feature extraction and sample region restoration of the original sample, and does single-sample expansion to achieve feature expansion for the extracted features, and finally does Poisson Fusion to achieve the fusion of features and repaired samples to generate New samples and complete the equalization of the sample space. AC Model uses spatially balanced new samples as the input of the detection model, and the deep residual idea to construct the detection model, and integrates the attention mechanism to improve the model′s learning ability of defect features. The present model focuses on solving the problem of unbalanced original sample space in surface defect detection of workpiece, and improves the feature learning ability and robustness of the detection model. Finally, five types of workpiece image samples are used to complete the experimental comparison, which verifies the effectiveness of this method and feasibility, it provides a new idea for surface defect detection in unbalanced sample space.