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

Huo, Lintao; Liu, Ying; Yang, Yutu; Zhuang, Zilong; Sun, Mengmeng

doi:10.1177/16878132231159523

Cited by 14 publications

(6 citation statements)

References 98 publications

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“…In these circumstances, the working accuracy and efficiency of the workpiece decrease, subsequently shortening its service life. This greatly increases the probability of safety risks associated with the application of defective workpeice [ 4 ]. Therefore, defect detection of the workpiece surface as a crucial step of production [ 5 , 6 ] can timely identify surface quality issues, which is greatly avoiding the risks of accidents during usage.…”

Section: Introductionmentioning

confidence: 99%

Identification and classification of surface defects for digital twin models of the workpiece

Qu,

Huang,

Zhang

et al. 2024

PLoS ONE

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Workpiece surface defect detection is an indispensable part of intelligent production. The surface information obtained by traditional 2D image detection has some limitations due to the influence of environmental light factors and part complexity. However, the digital twin model has the characteristics of high fidelity and scalability, and the digital twin surface can be obtained by a device with a scanning accuracy of 0.02mm to achieve the representation of the real surface of the workpiece. The surface defect detection system for digital twin models is proposed based on the improved YOLOv5 model in this paper. Firstly, the digital twin model of the workpiece is reconstructed by the point cloud data obtained by the scanning device, and the surface features with defects are captured. Subsequently, the training dataset is calibrated based on the defect surface, where the defect types include Inclusion, Perforation, pitting surface and Rolled-in scale. Finally, the improved YOLOv5 model with CBAM mechanism and BiFPN module was used to identify the surface defects of the digital twin model and compare it with the original YOLOv5 model and other common models. The results show that the improved YOLOv5 model can realize the identification and classification of surface defects. Compared with the original YOLOv5 model, the mAP value of the improved YOLOv5 model has increased by 0.2%, and the model has high precision. On the basis of the same data set, the improved YOLOv5 model has higher recognition accuracy than other models, improving 11.7%, 3.4%, 6.2%, 33.5%, respectively. As a result, this study provides a practical and systematic detection method for digital twin model surface during the intelligent production process, and realizes the rapid screening of the workpiece with defects.

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Section: Introductionmentioning

confidence: 99%

Identification and classification of surface defects for digital twin models of the workpiece

Qu,

Huang,

Zhang

et al. 2024

PLoS ONE

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“…The three-dimensional morphological structure of plants can reflect the true state of plant growth and development. Establishing three-dimensional models of plants to study plants and accurately measure the height, volume, leaf inclination, and other parameters of plants have always been a research hotspot in botany, computer graphics, and other disciplines [25]. In this paper, we used the built-in functions of the commercial software Agisoft photoscan 1.3.2 (Agisoft LLC, ST, Petersburg, Russia) to detect feature points, generate sparse point clouds, and reconstruct dense point clouds in the above pre-processed image sequences.…”

Section: Three-dimensional Plant Reconstruction Based On Sfm-mvsmentioning

confidence: 99%

Three-Dimensional Quantification and Visualization of Leaf Chlorophyll Content in Poplar Saplings under Drought Using SFM-MVS

Tian,

Zhang,

Bian

et al. 2023

Forests

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As global temperatures warm, drought reduces plant yields and is one of the most serious abiotic stresses causing plant losses. The early identification of plant drought is of great significance for making improvement decisions in advance. Chlorophyll is closely related to plant photosynthesis and nutritional status. By tracking the changes in chlorophyll between plant strains, we can identify the impact of drought on a plant’s physiological status, efficiently adjust the plant’s ecosystem adaptability, and achieve optimization of planting management strategies and resource utilization efficiency. Plant three-dimensional reconstruction and three-dimensional character description are current research hot spots in the development of phenomics, which can three-dimensionally reveal the impact of drought on plant structure and physiological phenotypes. This article obtains visible light multi-view images of four poplar varieties before and after drought. Machine learning algorithms were used to establish the regression models between color vegetation indices and chlorophyll content. The model, based on the partial least squares regression (PLSR), reached the best performance, with an R2 of 0.711. The SFM-MVS algorithm was used to reconstruct the plant’s three-dimensional point cloud and perform color correction, point cloud noise reduction, and morphological calibration. The trained PLSR chlorophyll prediction model was combined with the point cloud color information, and the point cloud color was re-rendered to achieve three-dimensional digitization of plant chlorophyll content. Experimental research found that under natural growth conditions, the chlorophyll content of poplar trees showed a gradient distribution state with gradually increasing values from top to bottom; after being given a short period of mild drought stress, the chlorophyll content accumulated. Compared with the value before stress, it has improved, but no longer presents a gradient distribution state. At the same time, after severe drought stress, the chlorophyll value decreased as a whole, and the lower leaves began to turn yellow, wilt and fall off; when the stress intensity was consistent with the duration, the effect of drought on the chlorophyll value was 895 < SY-1 < 110 < 3804. This research provides an effective tool for in-depth understanding of the mechanisms and physiological responses of plants to environmental stress. It is of great significance for improving agricultural and forestry production and protecting the ecological environment. It also provides decision-making for solving plant drought problems caused by global climate change.

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“…Obviously, Scanning these kinds of objects provides different accuracies due to color's effects, quality of the surface and its materials type. (Huang et al, 2023), (Julin et al, 2020) (Huo et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

The influence of the incident angles variation on the accuracy of TLS point cloud based on surface reflectivity and roughness

Mala,

Al-shrafany

2024

PJBAS

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The accuracy of measuring TLS point clouds depends on the measuring travel time, range detector, and surface properties. TLS conducted interesting experimental tests to examine the effect of different scanned materials in terms of reflectivity and surface roughness on the quality of the measured TLS data at different incident angles. Different types of materials specifically (wood, glass, steel, Ekoplast, and adhesive total station target) were selected for this purpose. During the experiments, 24 scans were measured as the selected four materials scanned at six incident angles (0˚, 15˚, 30˚, 45˚, 60˚, and 75˚) while the range was fixed to about 5m. The experiment's results reveal that smooth surfaces are more highly affected by the accuracy of the measured 3D point clouds than rough surfaces at different incident angles. At 0˚ incident angle, the total station target reflects about 20cm closer to the scanner than the other materials; this difference decreases with increasing incident angle. At 75˚ incidence angle, the difference decreases to become approximately 2mm in comparison to the other materials. The maximum RMSE of rough materials is less than 1cm except for wood material at 30˚, while the Maximum RMSE for smooth materials reaches 4cm in 45˚ glass material. Furthermore, different materials have different intensities, both smooth materials, glass and steel, have different levels of accuracy due to different properties. The minimum RMSE in glass is 1.47cm, and the maximum RMSE for steel reaches 1.17cm.

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

Cited by 14 publications

References 98 publications

Identification and classification of surface defects for digital twin models of the workpiece

Identification and classification of surface defects for digital twin models of the workpiece

Three-Dimensional Quantification and Visualization of Leaf Chlorophyll Content in Poplar Saplings under Drought Using SFM-MVS

The influence of the incident angles variation on the accuracy of TLS point cloud based on surface reflectivity and roughness

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