id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-12714	Liu, Yijun; Wu, Wei; Ren, Xiaolin; Qin, Le; Wang, Yukun	Research on Geometric Parameter Prediction Algorithm for Oil and Gas Pipeline Defects	2023	6	.pdf	application/pdf	4041	190	43	China Abstract: Aiming at the problem of insufficient feature extraction of magnetic leakage signals by traditional neural networks, this paper proposes an attention depth convolutional neural network model (ECA-VGG16), which adds an attention mechanism combined with convolutional neural network on the basis of deep convolutional neural network VGG16, so that the neural network can focus on the key information of the input data, further improve the ability of the grid to extract image data features, and realize the accurate expression of defect features. The deep learning network model designed for intelligent diagnosis of pipeline magnetic flux leakage detection defects is based on VGG16 and composed of Laplace pyramid[5].	cache/ajst-12714.pdf	txt/ajst-12714.txt