3D inspection technology combining passive stereo matching and active structured light for steel plate surface sample

2017 ◽  
Vol 11 (4) ◽  
pp. 299
Author(s):  
Yunhui Yan ◽  
Zhipeng Dong ◽  
Kechen Song ◽  
Menghui Niu ◽  
Xin Wen
Keyword(s):  
Stereo Matching ◽  
Steel Plate ◽  
Structured Light ◽  
Plate Surface ◽  
Surface Sample ◽  
Passive Stereo

The Study on High Precision 3D Detection Method of Steel Plate Surface Based on Structured Light

2012 ◽  
Vol 572 ◽  
pp. 338-342 ◽  
Author(s):  
Zhi Guo Liang ◽  
Quan Yang ◽  
Ke Xu ◽  
Fei He ◽  
Xiao Chen Wang ◽  
...  
Keyword(s):  
Surface Quality ◽  
Steel Plate ◽  
Detection Method ◽  
Structured Light ◽  
Detection Methods ◽  
Measurement Technology ◽  
Detection Accuracy ◽  
Plate Surface ◽  
Factors Affecting

Structured light 3D measurement technology with its simple structure, non-contact measurement, fast measurement speed and other advantages, has been widely used. Steel plate surface quality detection is not confined to the two-dimensional feature of gray detection, and local topography measurement for surface quality of steel plate detection becomes increasingly important. In this paper, steel plate surface 3D detection method based on structured light and the factors affecting the measurement accuracy are analyzed. Several effective methods of improving 3D detection accuracy are put forward. Compared with the traditional structured light 3D detection methods, the detection accuracy of new methods is remarkably improved, thus possessing better application values.

Stereo computation combining structured light and passive stereo matching

2012 ◽  
Vol 285 (6) ◽  
pp. 1017-1022 ◽  
Author(s):  
Tomislav Pribanic ◽  
Nenad Obradovic ◽  
Joaquim Salvi
Keyword(s):  
Stereo Matching ◽  
Structured Light ◽  
Passive Stereo

scholarly journals DENSE 3D OBJECT RECONSTRUCTION USING STRUCTURED-LIGHT SCANNER AND DEEP LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 777-783
Author(s):  
V. V. Kniaz ◽  
V. A. Mizginov ◽  
L. V. Grodzitkiy ◽  
N. A. Fomin ◽  
V. A. Knyaz
Keyword(s):  
Optical Flow ◽  
3D Model ◽  
Stereo Matching ◽  
Structured Light ◽  
Primary Source ◽  
Ground Truth ◽  
Complex Surface ◽  
Systems Model ◽  
Structured Light Scanner ◽  
Starting Point

Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.

scholarly journals Thermographic Monitoring of Laser Cutting Machine

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 28
Author(s):  
Paolo Bison ◽  
Giovanni Ferrarini ◽  
Gabriele Zanon
Keyword(s):  
Temperature Field ◽  
Analytical Solution ◽  
Laser Cutting ◽  
Energy Demand ◽  
Steel Plate ◽  
Cutting Tools ◽  
Plate Surface ◽  
Ir Camera ◽  
Cutting Machine ◽  
Contact Processing

Computer Numerical Controlled (CNC) laser cutting tools are developing as an alternative to conventional cutting systems thanks to increased accuracy, non-contact processing, higher productivity, less energy demand. An IR camera is utilized to monitor the laser cutting process of a steel plate. Even though the process is very complicated an analytical solution of the temperature field generated on a slab by a point source moving along one direction of the plate surface is provided in order to interpret the temperature field experimentally obtained by the IR camera.

An Extraction Method Based on Invariance Geometric Feature

2015 ◽  
Vol 713-715 ◽  
pp. 1570-1573
Author(s):  
Rong Fen Gong ◽  
Mao Xiang Chu ◽  
Yong Hui Yang
Keyword(s):  
Extraction Method ◽  
Steel Plate ◽  
The Other ◽  
Geometric Feature ◽  
Geometric Features ◽  
The Novel ◽  
Statistical Features ◽  
Plate Surface ◽  
Shape Features ◽  
Defects Classification

An extraction method based on invariance geometric feature is proposed in this paper. This method extracts two types of feature from the object in an image. One type is five invariance statistical features of edge distance. The other is two invariance shape features: rectangular similarity feature and circular similarity feature. Moreover, this proposed method is used to extract defect features for steel plate surface. Its performance is tested in scale and rotation invariance and defects classification. Experimental results show that the novel geometric features have the ability of invariance and can improve the accuracy of classification.

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