Prof. Cheng Wu Profile

Prof. Cheng Wu

at Soochow Univ

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Publications (2)

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Proceedings Article | 23 November 2024 Paper

A parameter-free method for laser point cloud denoising based on texture information

Yufei Que, Jie Xie, Jin Zhang, Cheng Wu

Proceedings Volume 13239, 132390G (2024) https://doi.org/10.1117/12.3035577

KEYWORDS: Point clouds, Denoising, Model based design, Data processing

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Point cloud denoising is a crucial step in the processing of 3D data. Although model-based methods for point cloud denoising have seen some success, their performance often remains inconsistent due to the complex parameter selection required for objects with varying shapes. To address this challenge, we propose a new method called TPDn, which takes full advantage of the object geometry to select parameters automatically. After converting point clouds into a triangular mesh, TPDn defines two key textures: the normalized mesh size and the mesh normal. By fusing these two texture features, a global distribution function is established. TPDn adaptively determines the appropriate threshold by deriving it from a simple approximation function.

Proceedings Article | 25 March 2024 Paper

Crack detection based on mask RCNN and trajectory similarity measurement

Jinglong Lv, Xu Xu, Cheng Wu, Weilong Niu

Proceedings Volume 13089, 130890P (2024) https://doi.org/10.1117/12.3021454

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Concrete crack detection can reflect the condition of concrete structure in time, facilitate the staff to arrange maintenance, and is crucial to ensure the normal operation of facilities. Traditional manual concrete crack detection methods have been unable to meet the current real needs. The development of deep learning has injected new vitality into crack detection. However, due to the splicing marks of concrete structures, some fake cracks similar to real cracks will inevitably occur, and most of the existing deep learning models cannot effectively identify them. In order to enhance the reliability of deep learning detection of concrete cracks, this paper proposes a crack identification method based on Mask RCNN and track similarity measurement. By extracting the crack Mask results output by Mask RCNN, the track morphological characteristics of all cracks are established, and the authenticity of the crack results output by the model is further determined by track similarity measurement. In addition, in order to ensure the accuracy of model detection, this study carried out horizontal and vertical inversion, brightness adjustment and background color change data enhancement on the training data set, and introduced tunnel crack data collected under the condition of strong light source in dark environment. Through the experimental verification on the data set and real environment, the experimental results show that the method can effectively identify the true and false cracks and has advantages in improving the model detection of cracks.

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