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Blair J, Stephen B, Brown B, McArthur S, Gorman D, Forbes A, Pottier C, McAlorum J, Dow H, Perry M. Photometric stereo data for the validation of a structural health monitoring test rig. Data Brief 2024; 53:110164. [PMID: 38375140 PMCID: PMC10875225 DOI: 10.1016/j.dib.2024.110164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/22/2023] [Accepted: 01/31/2024] [Indexed: 02/21/2024] Open
Abstract
Photometric stereo uses images of objects illuminated from various directions to calculate surface normals which can be used to generate 3D meshes of the object. Such meshes can be used by engineers to estimate damage of a concrete surface, or track damage progression over time to inform maintenance decisions. This dataset [1] was collected to quantify the uncertainty in a photometric stereo test rig through both the comparison with a well characterised method (coordinate measurement machine) and experiment virtualisation. Data was collected for 9 real objects using both the test rig and the coordinate measurement machine. These objects range from clay statues to damaged concrete slabs. Furthermore, synthetic data for 12 objects was created via virtual renders generated using Blender (3D software) [2]. The two methods of data generation allowed the decoupling of the physical rig (used to light and photograph objects) and the photometric stereo algorithm (used to convert images and lighting information into 3D meshes). This data can allow users to: test their own photometric stereo algorithms, with specialised data created for structural health monitoring applications; provide an industrially relevant case study to develop and test uncertainty quantification methods on test rigs for structural health monitoring of concrete; or develop data processing methodologies for the alignment of scaled, translated, and rotated data.
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Affiliation(s)
- Jennifer Blair
- Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
- National Physical Laboratory, Teddington, TW11 0LW, UK
| | - Bruce Stephen
- Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
| | - Blair Brown
- Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
| | - Stephen McArthur
- Institute for Energy and Environment, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
| | - David Gorman
- National Physical Laboratory, Teddington, TW11 0LW, UK
| | | | | | - Jack McAlorum
- Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
| | - Hamish Dow
- Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
| | - Marcus Perry
- Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, G1 1XJ, UK
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Cao Y, Ding B, Chen J, Liu W, Guo P, Huang L, Yang J. Photometric-Stereo-Based Defect Detection System for Metal Parts. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22218374. [PMID: 36366075 PMCID: PMC9655976 DOI: 10.3390/s22218374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/27/2022] [Accepted: 10/27/2022] [Indexed: 05/27/2023]
Abstract
Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts' geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.
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Affiliation(s)
- Yanlong Cao
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
| | - Binjie Ding
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jingxi Chen
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
| | - Wenyuan Liu
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
| | - Pengning Guo
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
| | - Liuyi Huang
- Zhejiang Academy of Special Equipment Science, Zhejiang University, Hangzhou 310023, China
- Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province, Hangzhou 310023, China
| | - Jiangxin Yang
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
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A CNN Based Approach for the Point-Light Photometric Stereo Problem. Int J Comput Vis 2022. [DOI: 10.1007/s11263-022-01689-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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