基于改进对比学习的道路裂缝图像分类

道路裂缝是路面破损的重要组成部分,而道路裂缝分类可以对道路养护策略的制定进行针对性的安排.针对人工标注分类耗时长,效率低等问题,本文提出了一个基于对比学习的道路裂缝图像分类方法,在传统的对比学习框架中,对特征提取部分进行改进,使得模型对细小裂缝的特征更敏感.首先对进行数据增强,其次在特征提取部分对ResNet50的部分进行改进,使用多尺度的方法提取特征;再使用多层感知机(MLP)对提取到的特征进行降维处理,并投影到向量空间;最后使用余弦相似度与用归一化温度标度的交叉熵损失对模型进行优化.实验结果表明,改进后的模型比原模型在裂缝图像上的分类效果提高了0.22%,达到了92.1%,对裂缝图像分类有较好的效果.

Road Crack Image Classification Based on Improved Contrastive Learning

Road cracks are the key element of road damage, the classification of which can be used to arrange the formulation of road maintenance strategy in targeted ways. As the classification with manual annotation is time-consuming and inefficient, this study proposes a road-crack image classification method based on contrastive learning. In the traditional contrastive learning framework, the feature extraction part is improved to make the model more sensitive to the features of small cracks. Firstly, the data is augmented; the ResNet50 part is improved in feature extraction, and the multi-scale method is used to extract features. Then, multilayer perception (MLP) is employed to reduce the dimensions of extracted features and project them onto vector space. Finally, cosine similarity and cross-entropy loss of normalized temperature scale are applied to optimize the model. The experimental results reveal that compared with the original model, the improved model has a classification effect of 92.1% on crack images, an increase of 0.22%, which indicates it has a good effect on crack image classification.