Search

Your search keyword '"Zhang, Liqiang"' showing total 325 results
Start Over Author "Zhang, Liqiang" Publication Year Range This year
325 results on '"Zhang, Liqiang"'

1. Cycle-YOLO: A Efficient and Robust Framework for Pavement Damage Detection

Author

Li, Zhengji, Xiao, Xi, Xie, Jiacheng, Fan, Yuxiao, Wang, Wentao, Chen, Gang, Zhang, Liqiang, and Wang, Tianyang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

With the development of modern society, traffic volume continues to increase in most countries worldwide, leading to an increase in the rate of pavement damage Therefore, the real-time and highly accurate pavement damage detection and maintenance have become the current need. In this paper, an enhanced pavement damage detection method with CycleGAN and improved YOLOv5 algorithm is presented. We selected 7644 self-collected images of pavement damage samples as the initial dataset and augmented it by CycleGAN. Due to a substantial difference between the images generated by CycleGAN and real road images, we proposed a data enhancement method based on an improved Scharr filter, CycleGAN, and Laplacian pyramid. To improve the target recognition effect on a complex background and solve the problem that the spatial pyramid pooling-fast module in the YOLOv5 network cannot handle multiscale targets, we introduced the convolutional block attention module attention mechanism and proposed the atrous spatial pyramid pooling with squeeze-and-excitation structure. In addition, we optimized the loss function of YOLOv5 by replacing the CIoU with EIoU. The experimental results showed that our algorithm achieved a precision of 0.872, recall of 0.854, and mean average precision@0.5 of 0.882 in detecting three main types of pavement damage: cracks, potholes, and patching. On the GPU, its frames per second reached 68, meeting the requirements for real-time detection. Its overall performance even exceeded the current more advanced YOLOv7 and achieved good results in practical applications, providing a basis for decision-making in pavement damage detection and prevention.

Published
2024

10. War city profiles drawn from satellite images

Author

Hou, Zhengyang, Qu, Ying, Zhang, Liqiang, Liu, Jun, Wang, Faqiang, Yu, Qiwei, Zeng, An, Chen, Ziyue, Zhao, Yuanyuan, Tang, Hong, Wang, Yuebin, Li, Xingang, Li, Yang, Peng, Shuwen, Ran, Jing, Yao, Xin, Meng, Xichen, Liu, Suhong, and Zhou, Chenghu

Published
2024
Full Text
View/download PDF

15. GMoD: Graph-Driven Momentum Distillation Framework with Active Perception of Disease Severity for Radiology Report Generation

Author

Xiang, ZhiPeng, Cui, ShaoGuo, Shang, CaoZhi, Jiang, Jingfeng, Zhang, Liqiang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor

Published
2024
Full Text
View/download PDF

18. DNNobfus: a study on obfuscation-based edge-side model protection framework

Author

SONG Feiyang, ZHAO Xinmiao, YAN Fei, CHENG Binlin, ZHANG Liqiang, YANG Xiaolin, and WANG Yang

Subjects
artificial intelligence safety, code obfuscation, reverse engineering, model protection, Electronic computers. Computer science, QA75.5-76.95
Abstract

The proliferation of artificial intelligence models has rendered them vulnerable to a myriad of security threats. The extensive integration of deep learning models into edge devices has introduced novel security challenges. Given the analogous structural characteristics of deep neural networks, adversaries can employ decompilation tactics to extract model structural details and parameters, facilitating the reconstruction of these models. Such actions can compromise the intellectual property rights of the model and increase the risk of white-box attacks. To mitigate the capability of model decompilers to locate and identify model operators, acquire parameters, and parse network topologies, an obfuscation framework was proposed. This framework was embedded within the model compilation process to safeguard against model extraction attacks. During the frontend optimization phase of deep learning compilers, three obfuscation techniques were developed and integrated: operator obfuscation, parameter obfuscation, and network topology obfuscation. The framework introduced opaque predicates, incorporated fake control flows, and embedded redundant memory access to thwart the reverse engineering efforts of model decompilers. The experimental findings demonstrate that the obfuscation framework, named DNNobfus, significantly diminishes the accuracy of state-of-the-art model decompilation tools in identifying model operator types and network connections to 21.63% and 48.24%, respectively. Additionally, DNNobfus achieves an average time efficiency of 67.93% and an average space efficiency of 88.37%, surpassing the performance of the obfuscation tool Obfuscator-LLVM in both respects.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results