基于改进BiFPN的微特电机电枢表面缺陷检测方法

针对现有微特电机电枢表面缺陷检测方法存在检测精度不高，特别是对相似性工件容易误判等问题，结合深度学习的方法，提出一种基于改进BiFPN的电枢外观缺陷检测方法。工业相机采集到的电枢图像通过匹配算法经过裁剪得到ROI，将ROI输入到EfficientNet结构，进行基础特征提取；采用通道注意力机制增强改进的BiFPN结构，对提取出的不同维度特征进行融合，并对特征进行筛选；使用分类器输出最终检测结果。结果表明：该电枢外观缺陷检测方法检测准确率优于ResNet和EfficientNet等深度学习检测方法，其检测准确率高达98.42%。研究结果对相似性较大的微特非标工件的检测性能提升有积极意义。

Micro-motor Armature Surface Defect Detection Method Based on Improved BiFPN

To address the problems of low detection accuracy of existing micro-motor armature surface defect detection methods,especially the misjudgment of similar workpiece,a new surface defect detection method for the armature based on improved BiFPN was proposed combined with deep learning method.The armature ROI image collected by the industrial camera was obtained by matching algorithm after cutting,the ROI was input into the EfficientNet structure,and the basic feature was extracted;the extracted features of different dimensions were fused by the improved BiFPN structure,and the features were screened;the classifier was used to output the final detection results.The results show that the armature surface defect detection method is more accurate than deep learning methods such as ResNet and EfficientNet,the accuracy is as high as 98.42%.The research results have positive significance to improve the detection performance of micro non-standard workpiece with great similarity.