East China Jiaotong University

Fangping Xu

Rongxiu Lu

Gang Yang

Lizhen Dai

Jianyong Zhu

Quanheng He

Yuyu Weng

Zhipeng Yang

Cailing Tang

Shuo Xu

Jie Sheng

Hailong Yang

Provided is an image inpainting method and system guided by an image structure and texture information. The method includes: constructing an image inpainting model with an interaction ability between the image structure and the texture information; preprocessing a damaged image sample to obtain a mask image sample and an edge structure image sample; inputting the damaged image sample, the mask image sample, and the edge structure image sample into the image inpainting model, training the image inpainting model, and generating weight information of the image inpainting model, to obtain a target image inpainting model; obtaining a damaged image to be restored, preprocessing the damaged image to be restored, to obtain a mask image and an edge structure image, separately inputting the damaged image to be restored, the mask image, and the edge structure image into the target image inpainting model, outputting, by the target image inpainting model, a restored image.

Image inpainting method and system guided by image structure and texture information

A fault detection method and system for tunnel dome lights based on an improved localization loss function. The method includes: constructing a dataset of tunnel dome light detection images; acquiring a you only look once (YOLO) v5s neural network based on the improved localization loss function; training the YOLO v5s neural network according to the dataset to obtain a trained YOLO v5s neural network; acquiring a to-be-detected tunnel dome light image; detecting, with the trained YOLO v5s neural network, the to-be-detected tunnel dome light image to obtain position coordinates of luminous dome lights; and determining, according to the position coordinates of the luminous dome lights, whether a fault occurs in the tunnel dome lights. The present disclosure can accurately localize the tunnel dome lights and label the positions, and can detect whether the tunnel dome lights work normally according to a relative positional relationship between the labeled dome lights.

Fault detection method and system for tunnel dome lights based on improved localization loss function

Described is a method for predicting multiple components' content in a case that rare earth ions with and without color feature coexist, and relates to component content prediction in rare earth extraction process. It is difficult to quickly/accurately detect component's content in rare earth extraction process. Because of relatively large difference between images' color features of CePr/Nd mixed solution with colorless Ce ions and Pr/Nd solution, detecting content method of single rare earth element based on color feature is no longer applicable. The method includes: first searching for H and S components with maximum correlation with component content in HSI color space; establishing ELM based multi-component content soft measurement model using H and S component first-order moment as input; and for uncertainty of initial weight and ELM (extreme learning machine) model's threshold, optimizing model parameters using genetic algorithm GA to optimize ELM model for component content prediction higher precision.

Method and system for predicting content of multiple components in rare earth extraction process

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Nanchang, China

Fangping Xu