id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-29623	Yan, Kaiqi; Wu, Wei; Wei, Hangxin; Sun, Jinghao	Geometric Parameter Evaluation of Oil and Gas Pipeline Defects Based on Deep Learning Neural Networks	2025	6	.pdf	application/pdf	3186	181	54	The axial and radial MFL data of pipeline defects are used as inputs to the model, and the length, width, and depth of defects are output in parallel according to the characteristics of MFL data, thus achieving the assessment and prediction of defect sizes. However, due to the correlation between the leakage magnetic field generated by pipeline defects and the various size parameters of defects, it affects the learning efficiency of single task models for defect quantification tasks.	cache/ajst-29623.pdf	txt/ajst-29623.txt