基于双线性CNN与DenseBlock的导光板标记线缺陷检测

导光板标记线检测是导光板制造品控中的一个重要步骤, 但在使用传统图像算法进行检测的过程中, 有大量的气泡、严重污染和无标记线的情况存在. 因有大量气泡, 严重污染和无标记线的情况造成人工特征难以设计, 因此, 使用基于卷积网络的方法来代替人工特征设计进行缺陷检测. DenseNet 卷积神经网络较其他分类神经网络具有参数较少, 梯度收敛稳定等特点. 因DenseNet 卷积神经网络中使用特征融合的思想, 保证了图片分类准确率. 通过迁移学习的方法, 将训练得到的DenseNet 网络权重迁移到Bilinear-CNN算法进行训练, 提升卷积神经网络局部注意力, 提高图像分类准确率. 通过实现结果表明, 所提方法具有可行性, 相比于V2-ResNet-101网络结构, 准确率提升至95.53%, 参数减少了97.2%, 平均单张图像检测时间减少25%.

LGP Marker Defect Detection Algorithm Based on Bilinear-CNN and DenseBlock

Light Guide Plate (LGP) marker detection is a crucial procedure in LGP manufacturing quality control but a mass of bubbling, polluted and marker-free cases may arise out of detection in traditional image algorithms. Because the mass of bubble, polluted and unmarked lines makes people difficult to design features. Compared with other classification neural networks, DenseNet Convolution Neural Network (CNN) has fewer parameters and stable gradient convergence. Because DenseNet CNN uses the idea of feature fusion, the accuracy of image classification is guaranteed. Through the transform learning method, the weights of the trained DenseNet network are transferred to the bilinear-CNN algorithm for training, which improves the local attention of the convolutional neural network and improves the accuracy of image classification. The implementation results show that the proposed method is feasible. Compared with the V2-ResNet-101, the accuracy of proposed approach is increased to 95.53%, while parameter number is decreased by 97.2%, and average single image detection time drops by 25% in the proposed network structure.