A Study on Extraction Method of Spotted Surface Defects by Using Deep Learning From Corrected 3-D Point Clouds Profile Data

Eguchi, Masayuki; Kawamura, Akira; Tomiyama, Kazuya; Takahashi, Shigeki; Omachi, Shinichiro

doi:10.2208/jscejpe.74.i_121

Cited by 3 publications

(1 citation statement)

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“…In the detection of weld defects, in order to enhance the presentation effect of weld defects, Shao et al 97 used six depth images projected from different angles of the solder joint point cloud model as the input of the model, and used standard CNN to detect and classify solder joint defects, which made up for the 85 shortcomings of insufficient global feature capture ability of point cloud based on depth image. Eguchi et al 98 transformed the 3D point cloud data of the pavement into color information images, and used the AlexNet model to detect the spots on the asphalt surface. In order to obtain the detection effect of the optimal 3D point cloud image, Jiang et al 78 compared the performance of RGB, depth and normal vectors and their combined feature images from photogrammetry point clouds for image segmentation tasks on the U-Net model.…”

Section: Damage Detection Using Point Cloud Imagesmentioning

confidence: 99%

Review: Research on product surface quality inspection technology based on 3D point cloud

Huo

Liu

Yang

et al. 2023

Advances in Mechanical Engineering

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In recent years, machine vision has played an important role in product surface quality detection. The promotion and use of this technology have largely avoided the subjectivity caused by human detection and improved detection efficiency and accuracy. Different from the image data commonly used in machine vision, point cloud can describe the spatial structure of an object, provide more information than image data, and have the ability to expand the data to build multi-dimensional data models. Due to the strong anti-interference ability of point cloud sensors and the high accuracy of three-dimensional positioning information point cloud, nondestructive testing technology based on point cloud has received more and more attention. This paper summarizes the research progress of product surface quality detection methods based on 3D point cloud in recent years. According to different data processing methods, the detection research is divided into five categories: based on point cloud contour, based on local geometric feature, based on template matching, based on multimodal point cloud, and based on deep learning. The five methods are reviewed and summarized respectively. Finally, the key problems of each detection method and the future trend of product surface quality detection technology based on 3D point cloud are discussed.

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