针对轨枕挡肩裂纹图像精细化分类问题,提出了改进双线性卷积神经网络(B-CNN)模型。该模型先通过全局平均池化链接图像特征中的全局信息以捕捉细微裂纹宽度特征;再通过不同层次特征融合增强特征表达能力,获得有效宽度特征以实现细粒度分类。实验结果表明:该模型与B-CNN模型相比,分类准确率提升了2个百分点,在假阴性率方面,正常类别降低了2.3个百分点,明显裂纹类别降低了4.55个百分点;与基线VGG-D(Visual Geometry Group Network-D)模型相比,分类准确率提升6.11个百分点,在假阴性率方面,正常类别降低了7.39个百分点,明显裂纹类别降低了8.39个百分点,且参数量仅为原参数量的18.51%,特征提取速度提升了45.31%,说明该模型能够满足快速、准确对双块式轨枕挡肩裂纹图像分类的工程需求。

An improved bilinear convolutional neural network (B-CNN) model is proposed to solve the problem of fine-grained classification of crack images of sleeper block shoulder. Using this model, the global information in the image features of the global average pooling link is first used to capture the width information of the fine crack. Then, the fusion of different levels is performed to enhance the ability of feature expression to obtain effective width features and fine-grained classification. Experimental results show that compared with the B-CNN model, the classification accuracy of this model improves by 2 percentage. In terms of the false negative rate, the normal category reduces by 2.3 percentage, and the obvious crack category reduces by 4.55 percentage. Compared with the baseline VGG-D (Visual Geometry Group Network-D) model (6.11 percentage classification accuracy), the normal false negative rate reduces by 7.39 percentage, and obvious crack category reduces by 8.39 percentage. Furthermore, the feature extraction rate for the original is 18.51%, whereas that of our proposed model is 45.31%, which shows that the proposed model can satisfy the need for rapid and accurate imaging of the shoulder for double block-type sleeper crack image classification to meet engineering requirements.