基于软注意力机制的图像分类算法在缺陷检测中的应用

Application of Image Classification Algorithm Based on Soft Attention Mechanism in Defect Detection

针对传统表面缺陷检测算法检测效率低下,难以应对复杂性检测等问题,结合深度学习和注意力机制技术,提出一种新型注意力机制算法。首先,反思卷积神经网络(CNN)与Transformer架构,重新设计高维特征提取模块;其次,改进最新注意力机制来捕获全局特征。该算法可轻松嵌入各类CNN,提升图像分类和表面缺陷检测的性能。使用该算法的Res Net网络在CIFAR-100数据集和纺织品缺陷数据集上的准确率分别达到83.22%和77.98%,优于经典注意力机制SE与最新的Fca等方法。

Aiming at the problems of traditional surface defect detection algorithms,such as low detection efficiency and it has difficulty to deal with complexity detection,a new attention mechanism algorithm is proposed by combining deep learning and attention mechanism technology.First,rethink profoundly the Convolutional Neural Networks(CNN)and Transformer architecture,and redesign the high-dimensional feature extraction module;secondly,improve the latest attention mechanism to capture global features.This algorithm can easily embed various CNN,improve the performance for image classification and surface defect detection.The accuracy of the ResNet network using this algorithm on the CIFAR-100 data set and the textile defect data set reaches 83.22%and 77.98%respectively,which is superior to the classical attention mechanism SE and the latest Fca and other methods.