Point Cloud-Based Concrete Surface Defect Semantic Segmentation Using Modified PointNet++

Bolourian, Neshat; Hammad, Amin; Ghelman, Ali

doi:10.7146/aul.455.c227

Cited by 2 publications

(1 citation statement)

References 14 publications

(15 reference statements)

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“…In work of Bolourian et al., 18 a method is proposed for detecting defects on concrete surfaces using a model based on PointNet++, 19 focusing on point cloud data. Along with point coordinates, this study uses color and surface normal vectors, anticipating significant differences of these in areas with cracks, with the aim to enhance defect segmentation accuracy and reliability.…”

Section: Previous Workmentioning

confidence: 99%

Domain shared features for visual inspection of complex mechanical assemblies based on synthetically generated data

Došljak,

Jovančević,

Orteu

2024

J. Electron. Imag.

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Even though neural network methodologies have been established for a long time, only recently have they achieved exceptional efficacy in practical deployments, predominantly due to improvements in hardware computational capacity and the large amounts of available data for learning. Nonetheless, substantial challenges remain in utilizing deep learning in many domains, mainly because of the lack of large amounts of labeled data that are versatile enough for deep learning models to learn useful information. For instance, in mechanical assembly inspection, annotating data for each type of mechanical part to train a deep learning model can be very labor-intensive. Additionally, it is required to annotate data after each modification of mechanical part specification. Also, the system for inspection is typically not available until the first few samples are built to collect data. This paper proposes a solution for these challenges in case of the visual mechanical assembly inspection by processing point cloud data acquired via a three-dimensional (3D) scanner. To reduce the necessity for manually labeling large amounts of data, we employed synthetically generated data for both training and validation purposes, reserving the real sensor data exclusively for the testing phase. Our approach reduces the need for large amounts of labeled data by using synthetically generated point clouds from computer-aided design models for neural network training. Domain gap is a significant challenge for the usage of synthetically generated data. To reduce the domain gap, we used different preprocessing techniques, as well as a neural network architecture that focuses more on shared features that will not significantly change between synthetically generated data and real data from the 3D sensor.

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Section: Previous Workmentioning

confidence: 99%

Domain shared features for visual inspection of complex mechanical assemblies based on synthetically generated data

Došljak,

Jovančević,

Orteu

2024

J. Electron. Imag.

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Crack Detection and Feature Extraction of Heritage Buildings via Point Clouds: A Case Study of Zhonghua Gate Castle in Nanjing

Wang,

Shi,

Yuan

et al. 2024

Buildings

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Zhonghua Gate Castle is on the tentative list for Chinese World Cultural Heritage. Due to long-term sunshine, rain erosion, and man-made damage, its surface appears to have different degrees of cracks and other diseases. This paper centers on Zhonghua Gate Castle; terrestrial laser scanning is used to obtain the exterior wall point cloud data. A crack detection method based on point cloud data curved surface reconstruction is proposed. It involves data preprocessing, crack detection, and the analysis of crack features. This method initially uses data preprocessing techniques to improve data quality. These techniques include removing ground points and super-voxel segmentation. Subsequently, local surface reconstruction was employed to address the issue of missing point cloud data within cracks and the Euclidean clustering algorithm was used for precise crack identification. The article provides a detailed analysis of the geometric characteristics of cracks. They involve the calculation of length, width, and area. The results of the experiment demonstrate that the method could successfully identify cracks and extract geometric features and has millimeter-level accuracy compared to actual crack sizes.

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Point Cloud-Based Concrete Surface Defect Semantic Segmentation Using Modified PointNet++

Cited by 2 publications

References 14 publications

Domain shared features for visual inspection of complex mechanical assemblies based on synthetically generated data

Domain shared features for visual inspection of complex mechanical assemblies based on synthetically generated data

Crack Detection and Feature Extraction of Heritage Buildings via Point Clouds: A Case Study of Zhonghua Gate Castle in Nanjing

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