基于多支路卷积神经网络的磁瓦表面缺陷检测算法

Surface defect detection algorithm of magnetic tiles based on multi-branch convolutional neural network

针对磁瓦表面缺陷检测难度大和精度低的问题,提出了一种新的磁瓦表面缺陷检测算法。首先,设计了一种多支路网络结构,并在各支路中构建了一种能有效提取磁瓦图像特征的卷积神经网络;然后,引入注意力模块突出图像的重要特征;最后,通过判别相关分析使同类特征的相关性和不同类特征的差异性最大化,并通过级联融合得到优化的磁瓦图像融合特征。在磁瓦图像数据集上,对算法检测性能进行了测试,测试精度达到99.90%;在实际检测工作中,本文算法的检测准确率保持在99%以上,检测速度达到129块/min。实验和运行结果表明:本算法检测精度高,性能稳定可靠,能满足磁瓦大批量生产实时在线检测要求。

Aiming at the problem of great difficulty and low accuracy in the detection of magnetic tile surface defects(DMTSD),a novel algorithm of DMTSD is proposed.In this paper,a multi-branch network structure is designed,and a convolutional neural network which can effectively extract the features of the magnetic tile images is constructed in each branch,and then the attention module is introduced to highlight the important features of the image.Finally,the correlation of the intra-class features and the difference of the inter-class features is maximized through discriminant correlation analysis,and the optimized fusion feature of magnetic tile images is obtained by concatenation fusion.The performance of the algorithm is tested on the magnetic tile image data set,and the test accuracy is up to 99.90%.In the actual detection work,the detection accuracy of the algorithm remains above 99%,and the detection speed reaches 129 pcs/min.The results of test and operation show that the algorithm has the advantage of high detection accuracy and stable performance,and can meet the requirements of real-time online detection in mass production of magnetic tiles.