Dr. Jian Wang Profile

Dr. Jian Wang

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Proceedings Article | 19 January 2024 Paper

Urban modern architecture recognition based on Mask-RCNN and ECA attention mechanism

Xiaofan Zhu, Lujin Hu, Jian Wang

Proceedings Volume 12980, 129801D (2024) https://doi.org/10.1117/12.3021182

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Modern architecture plays an essential role in the fields of land surveying and mapping, urban planning and change detection. Aiming at the problems of large workload, long cycle and poor timeliness of traditional field surveying and mapping of modern architectures, this paper proposes the Mask-RCNN network model and introduce the ECA attention mechanism to reflect the self-made street view image data set of building facade information, to quickly and accurately identify urban modern architectures. And compared with SVM, U-net and Mask-RCNN building extraction algorithms. Experiments show that the proposed method can extract modern architectures efficiently and accurately. For the same data set, the extraction result is 2.6 % higher than the original Mask-RCNN algorithm and is better than the comparison algorithm.

Proceedings Article | 10 February 2023 Paper

The influence of traffic vibration on Great Wall deformation is monitored by accelerometer

Zhe Cui, Xu Liu, Fei Liu, Jian Wang

Proceedings Volume 12552, 125521H (2023) https://doi.org/10.1117/12.2667489

KEYWORDS: Vibration, Roads, Accelerometers, Spectral density, Denoising, Safety, Deformation, Time-frequency analysis, Spectrum analysis, Video surveillance

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With the rapid development of the modern transportation network, the phenomenon of roads crossing the Great Wall is increasing day by day, making ground traffic vibration an important factor affecting the safety of the Great Wall. This paper firstly analyzes the correlation between traffic flow and the vibration acceleration of enemy stations on the Great Wall; then establishes a vibration data denoising method based on variational mode decomposition (VMD) combined with FLANDRIN criterion, and removes the high-frequency noise of vibration acceleration; To denoise the acceleration data, an integrated VMD and Hilbert-Huang transform (HHT) time-frequency feature extraction model was introduced to extract the instantaneous vibration frequency and intensity. The results show that the ground traffic vibration has a great influence on the short-cut Great Wall, which leads to the vibration of the enemy stations on the Great Wall, and the vibration frequency is 0.27 Hz; the VMD-HHT model can accurately obtain the instantaneous vibration frequency and intensity characteristics of the enemy stations on the Great Wall. This research can provide an important reference for the real-time safety monitoring and protection of the Great Wall.

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