关键词: 表面缺陷检测, 掩膜区域卷积网络, 特征金字塔网络, 磁瓦

Abstract: The magnetic tile image has the characteristics of uneven illumination,.complex surface texture and low contrast,but its defects are hard segmented with the traditional defect detection algorithms.For this reason,a defect detection algorithm based on Mask Region-based Convolutional Network (Mask R-CNN) was proposed.The image was preprocessed by contrast limited adaptive histogram equalization.Then Residual Network 50 (ResNet50) was used to construct the Feature Pyramid Network (FPN) to acquire image information and extract features.Then,the region of interest of the defect region were extracted by Region Proposal Network (RPN) to obtain the corresponding anchor frame,and the pixel class inside the region of interest was predicted by Fully Convolutional Network (FCN) to realize the defect.Through the fully connected layer of the network,the prediction the category and the corresponding anchor frames of each interest region was realized.The experimental results showed that the proposed algorithm had strong generalization ability,and strong robustness.It could accurately segment the surface of the magnetic tile image with complex texture,uneven illumination and low contrast.

Key words: surface defect detection, mask region-based convolutional network, feature pyramid network, magnetic tile