基于自编码和知识蒸馏的表面缺陷检测方法

针对传统的表面缺陷检测方法只能对具有高对比度或低噪声的明显缺陷轮廓进行检测的问题，提出了一种基于自编码和知识蒸馏的表面缺陷检测方法来准确定位和分类从实际工业环境捕获的输入图像中出现的缺陷。首先，设计了一种级联自动编码器（CAE）架构用于分割和定位缺陷，其目的是将输入的原始图像转换为基于CAE的预测蒙版；其次，利用阈值模块对预测结果进行二值化以获得准确的缺陷轮廓；然后，把缺陷区域检测器提取并裁剪出来的缺陷区域视为下一个模块的输入；最后，将CAE分割结果的缺陷区域通过知识蒸馏进行类别分类。实验结果表明，与其他几种表面缺陷检测方法相比，所提出的方法综合性能最好，其缺陷检测平均准确率为97.00%。该方法能够有效地对较小的、边缘不清晰的缺陷进行分割，满足对物品表面缺陷实时分割检测的工程要求。

Surface defect detection method based on auto-encoding and knowledge distillation

The traditional surface defect detection methods can only detect obvious defect contours with high contrast or low noise. In order to solve the problem， a surface defect detection method based on auto-encoding and knowledge distillation was proposed to accurately locate and classify the defects that appeared in the input images captured from the actual industrial environment. Firstly， a new Cascaded Auto-Encoder （CAE） architecture was designed to segment and locate defects， whose purpose was to convert the input original image into the CAE-based prediction mask. Secondly， the threshold module was used to binarize the prediction results， thereby obtaining the accurate defect contour. Then， the defect area extracted and cropped by the defect area detector was regarded as the input of the next module. Finally， the defect areas of the CAE segmentation results were classified by knowledge distillation. Experimental results show that， compared with other surface defect detection methods， the proposed method has the best comprehensive performance， and its average accuracy of defect detection is 97.00%. The proposed method can effectively segment the smaller defects with blurred edges， and meet the engineering requirements for real-time segmentation and detection of item surface defects.