计算机与数字工程2023，Vol.51Issue(12)：2911-2917,7.DOI:10.3969/j.issn.1672-9722.2023.12.027
基于自监督预训练的小样本道路病害检测
Few-shot Road Disease Detection Based on Self-supervised Pre-training
摘要
Abstract
In the field of traffic road disease detection,problems of learning from few samples are often encountered,leading to the insufficient training of deep learning models.In order to solve the lack of supervisory signal with few-shot road data,a few-shot classification framework based on the self-supervised pre-trained model is proposed.Firstly,this framework can make full use of the all available data,including unlabeled samples,by forming pseudo labels according to the way of data augmentation.Then,the feature extractor can be pre-trained with pseudo labels.Secondly,the pre-trained parameters are transfered to the super-vised model,and the few-shot data with ground-truth labels is applied to fine-tune the whole model.Finally,the model will be used for road disease classification.In addition,the framework can be compatible with various feature extractors and few-shot met-ric measurements.The experiments use the real few-shot road data to test and analyze the self-supervised pre-training framework,which proves that this framework can effectively improve the accuracy of disease detection.关键词
道路病害/卷积神经网络/小样本学习/自监督学习Key words
road disease/convolution neural network/few-shot learning/self-supervised learning分类
信息技术与安全科学引用本文复制引用
韩海航,王月,周春鹏,王洋洋..基于自监督预训练的小样本道路病害检测[J].计算机与数字工程,2023,51(12):2911-2917,7.基金项目
浙江省重点研发计划项目(编号:2021C01106)资助. （编号:2021C01106）
