迁移学习在管道检测中的应用示例及分析

Application Examples and Analysis of Transfer Learning in Pipeline Inspection

基于人工智能,尤其是深度学习的方法对管道缺陷进行识别和分类,能够大幅提高工作效率,降低成本,应用效果良好。但实际工作中存在着当地模型泛化能力不强,在不同测区不同作业队伍不同作业方法下,模型精度下降甚至无法收敛的问题,应用迁移学习方法,在不同测区,不同视频设备、不同作业方法情况下,基于原有模型,增加少量本地数据进行迁移学习,快速建立适用于本测区的预测模型,具有重要的研究和实用价值,本文以两个城市、两种检测方法、三个作业队伍的数据进行了试验和分析,试验表明,迁移学习可以明显减少模型训练的样本和时间需求,显著提高了预测精度。对当前深度学习技术在管道检测领域应用的进一步发展具有参考意义。

Based on artificial intelligence,especially the deep learning method,it can realize the identification and classification of pipeline defects,which can greatly improve work efficiency,reduce production cost and have good application effect.However,in the actual work,there is a lack of local model generalization ability.Under different operation methods of different operation teams in different test areas,the accuracy of the model may not even converge,and the transfer learning method is applied.In different measurement areas,different video devices and different The operation method,using the original model,adding a small amount of local data for migration learning,and quickly establishing a prediction model suitable for the survey area has important research and practical value.This paper tests and analyzes data from two cities,two detection methods and three work teams.Experiments show that transfer learning can significantly reduce the sample and time requirements of model training and significantly improve the prediction accuracy.It has reference significance for the further development of pipeline testing applications in current deep learning technology.