activity
20182022
most citedA Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

eess.SP2022

Deep Learning based Intelligent Coin-tap Test for Defect Recognition

Hongyu Li, Peng Jiang, Tiejun Wang

The coin-tap test is a convenient and primary method for non-destructive testing, while its manual on-site operation is tough and costly. With the help of the latest intelligent si…

cs.CV20201 cited

A Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal

Hongyu Li, Jihe Wang, Yu Zhang +2

A reasonable evaluation standard underlies construction of effective deep learning models. However, we find in experiments that the automatic crack detectors based on deep learning…

cs.CV2020

Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification

Hongyu Li, Xiaogang Huang, Zhihui Fu +1

Preserving original noise residuals in images are critical to image fraud identification. Since the resizing operation during deep learning will damage the microstructures of image…

cs.LG2019

An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning

Hongyu Li, Tianqi Han

Federated learning is a distributed learning method to train a shared model by aggregating the locally-computed gradient updates. In federated learning, bandwidth and privacy are t…

cs.CV2019

Towards Document Image Quality Assessment: A Text Line Based Framework and A Synthetic Text Line Image Dataset

Hongyu Li, Fan Zhu, Junhua Qiu

Since the low quality of document images will greatly undermine the chances of success in automatic text recognition and analysis, it is necessary to assess the quality of document…

cs.CV2018

Two-Layer Mixture Network Ensemble for Apparel Attributes Classification

Tianqi Han, Zhihui Fu, Hongyu Li

Recognizing apparel attributes has recently drawn great interest in the computer vision community. Methods based on various deep neural networks have been proposed for image classi…