E-mail Alert
RSS
| Citation: |
Ni C Y, Zhang Y H, Zhu J J, et al. Reconstruction of the spatial temperature field based on geometric constraints under a thermal imager[J]. Opto-Electron Eng, 2024, 51(11): 240211. doi: 10.12086/oee.2024.240211
|
Reconstruction of the spatial temperature field based on geometric constraints under a thermal imager
-
Abstract
In view of the lack of spatial depth information in two-dimensional heat maps and the problems of complex calibration, high equipment cost, and limited use conditions in the existing multi-sensor schemes to establish the spatial temperature field model of the target object, this study proposes a spatial temperature field reconstruction scheme under a single thermal camera based on geometric constraints. Firstly, the temperature matrix collected by the thermal imager is remapped to the gray color space through the automatic threshold method, and the clear edge information of the target is obtained to filter out the irrelevant background. Then, the pose relationship of the target in the coordinate system of the thermal imager is directly calculated by the geometric constraints existing in the imaging process of the thermal imager and the imaging principle of the camera. Then, the 3D model of the target object is projected to the 2D heat map plane, and the texture mapping parameters of the 3D model are obtained. With the multi-view data acquisition of the target object by the thermal camera, the spatial temperature field reconstruction of the target object surface is completed. The experimental results show that, compared with the multi-sensor spatial temperature field reconstruction scheme, the average error of the single thermal imager scheme in this paper is only 4.3%, which can accurately and stably complete the spatial temperature field reconstruction of the target.-
Keywords:
- temperature model /
- thermal imagery /
- visual geometry /
- 3D reconstruction /
- texture mapping /
- temperature field
-
-
References
[1] 荆建行, 孔明东, 王强, 等. 基于红外热像仪的光学薄膜吸收测试方法[J]. 光电工程, 2021, 48(6): 210071. doi: 10.12086/oee.2021.210071 Jing J H, Kong M D, Wang Q, et al. Measurement of absorption loss of optical thin-film by infrared thermal imaging[J]. Opto-Electron Eng, 2021, 48(6): 210071. doi: 10.12086/oee.2021.210071 [2] 郑海君, 葛斌, 夏晨星, 等. 多特征聚合的红外-可见光行人重识别[J]. 光电工程, 2023, 50(7): 230136. doi: 10.12086/oee.2023.230136 Zheng H J, Ge B, Xia C X, et al. Infrared-visible person re-identification based on multi feature aggregation[J]. Opto-Electron Eng, 2023, 50(7): 230136. doi: 10.12086/oee.2023.230136 [3] 刘皓皎, 刘力双, 张明淳. 基于YOLOv5改进的红外目标检测算法[J]. 激光技术, 2024, 48(4): 534−541. doi: 10.7510/jgjs.issn.1001-3806.2024.04.011 Liu H J, Liu L S, Zhang M C. An improved infrared object detection algorithm based on YOLOv5[J]. Laser Technol, 2024, 48(4): 534−541. doi: 10.7510/jgjs.issn.1001-3806.2024.04.011 [4] Dlesk A, Vach K, Pavelka K. Photogrammetric co-processing of thermal infrared images and RGB images[J]. Sensors, 2022, 22(4): 1655. doi: 10.3390/s22041655 [5] 杨炎龙, 徐超. 一种人体体表三维温度场的融合重建方法[J]. 红外技术, 2022, 44(1): 33−40. doi: 10.11846/j.issn.1001-8891.2022.1.hwjs202201005 Yang Y L, Xu C. Fusion reconstruction method for 3D temperature fields on the human body surface[J]. Infrared Technol, 2022, 44(1): 33−40. doi: 10.11846/j.issn.1001-8891.2022.1.hwjs202201005 [6] 毕淳锴, 张远辉, 付铎. 基于多视角热像图序列的物体表面温度场重建[J]. 计量学报, 2024, 45(7): 997−1006. doi: 10.3969/j.issn.1000-1158.2024.07.09 Bi C K, Zhang Y H, Fu D. Surface temperature field reconstruction based on multi-view thermal image sequence[J]. Acta Metrol Sin, 2024, 45(7): 997−1006. doi: 10.3969/j.issn.1000-1158.2024.07.09 [7] 张远辉, 徐栢锐, 朱俊江, 等. 可见光与热像融合的三维温度模型重建[J]. 计量学报, 2022, 43(2): 256−263. doi: 10.3969/j.issn.1000-1158.2022.02.19 Zhang Y H, Xu B R, Zhu J J, et al. 3D temperature model reconstruction based on fusion of visible and thermal images[J]. Acta Metrol Sin, 2022, 43(2): 256−263. doi: 10.3969/j.issn.1000-1158.2022.02.19 [8] 张建华, 张天晶, 赵岩, 等. 面向室内动态场景的多传感视觉SLAM方法[J]. 信息与控制, 2022, 51(6): 641−650,661. doi: 10.13976/j.cnki.xk.2022.1436 Zhang J H, Zhang T J, Zhao Y, et al. Multi-sensor visual SLAM method for indoor dynamic scenes[J]. Inf Control, 2022, 51(6): 641−650,661. doi: 10.13976/j.cnki.xk.2022.1436 [9] Sentenac T, Bugarin F, Ducarouge B, et al. Automated thermal 3D reconstruction based on a robot equipped with uncalibrated infrared stereovision cameras[J]. Adv Eng Inf, 2018, 38: 203−215. doi: 10.1016/j.aei.2018.06.008 [10] Batchuluun G, Kang J K, Nguyen D T, et al. Deep learning-based thermal image reconstruction and object detection[J]. IEEE Access, 2021, 9: 5951−5971. doi: 10.1109/ACCESS.2020.3048437 [11] Cao Y P, Xu B B, Ye Z Y, et al. Depth and thermal sensor fusion to enhance 3D thermographic reconstruction[J]. Opt Express, 2018, 26(7): 8179−8193. doi: 10.1364/OE.26.008179 [12] Zhang Z Y. Flexible camera calibration by viewing a plane from unknown orientations[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999: 666–673. https://doi.org/10.1109/ICCV.1999.791289. [13] Canny J. A Computational approach to edge detection[J]. IEEE Trans Pattern Anal Mach Intell, 1986, PAMI-8(6): 679−698. doi: 10.1109/TPAMI.1986.4767851 [14] Otsu N. A Threshold selection method from gray-level histograms[J]. IEEE Trans Syst Man Cybern, 1979, 9(1): 62−66. doi: 10.1109/TSMC.1979.4310076 [15] Han P F, Zhao G. CAD-based 3D objects recognition in monocular images for mobile augmented reality[J]. Comput Graph, 2015, 50: 36−46. doi: 10.1016/j.cag.2015.05.021 [16] Garrido-Jurado S, Muñoz-Salinas R, Madrid-Cuevas F J, et al. Automatic generation and detection of highly reliable fiducial markers under occlusion[J]. Pattern Recognition, 2014, 47(6): 2280−2292. doi: 10.1016/j.patcog.2014.01.005 [17] Lepetit V, Moreno-Noguer F, Fua P. EPnP: an accurate O(n) solution to the PnP problem[J]. Int J Comput Vis, 2009, 81(2): 155−166. doi: 10.1007/s11263-008-0152-6 -
Overview
Thermal imagers can detect the heat distribution on the surface of an object through the reflection of thermal radiation, but two-dimensional heat maps lack spatial information. Establishing the space temperature field model of the target object can significantly increase the information dimension of non-contact temperature measurement and improve the temperature measurement quality. At present, most spatial temperature field reconstruction schemes are based on multi-sensor data fusion to make up for the defects of low texture and low resolution of thermal imaging. However, the existence of multiple sensors will increase the cost of the device, the difficulty of calibration, the complexity of the algorithm, and the constraints of the device. To solve the above problems, a reconstruction scheme of the space temperature field based on geometric constraints under a thermal imager is proposed. The temperature matrix is obtained by analyzing the data collected by the thermal imager and mapped to the gray space by the automatic threshold method. The interference factors are filtered out and the clear edge information of the object is calculated to complete the segmentation process. Then, according to the imaging principle of the thermal imager and the geometric constraints existing in the imaging process, the pose of the target in the thermal imager coordinate system is calculated directly, and the 3D model is projected into the 2D thermal map to get the mapping relationship between 3D and 2D. With the completion of multi-view data acquisition of the target object, the spatial temperature field reconstruction of the target object surface is finally completed, and its spatial temperature field model is obtained. The experimental results show that the proposed scheme can accurately and stably reconstruct the space temperature field of the target object with a small error. Finally, in the comparative experiment part, the proposed method is compared with the spatial temperature field reconstruction schemes under multi-sensor and single sensor respectively. Through the error analysis of the experimental data, it can be concluded that the proposed scheme has excellent performance in accuracy, stability and anti-interference. The proposed scheme also provides a new idea and a feasible scheme for the establishment and measurement of space temperature field under a single sensor.
-
Access History
Figures(22)
Tables(4)
Article Metrics
Export File
Citation
Ni C Y, Zhang Y H, Zhu J J, et al. Reconstruction of the spatial temperature field based on geometric constraints under a thermal imager[J]. Opto-Electron Eng, 2024, 51(11): 240211. doi: 10.12086/oee.2024.240211
Format
Content
DownLoad:
-
Figure 1.
Overall flowchart
-
Figure 2.
Calibration plates. (a) Positive view of the calibration plate; (b) Side view of the calibration plate; (c) Thermal imge of the calibration plate; (d) Binarization and inversion of the thermal image
-
Figure 3.
Angle iron and thermal images
-
Figure 4.
Contour extraction directly from thermal images
-
Figure 5.
Remapped heat map and profile extraction results
-
Figure 6.
Extracted contours of different targets, backgrounds, and distances
-
Figure 7.
Imaging principle
-
Figure 8.
Geometric constraints
-
Figure 9.
Geometric constraints in imaging
-
Figure 10.
Thermal imager and object coordinate system
-
Figure 11.
3D model projected onto a heat map
-
Figure 12.
Projection process
-
Figure 13.
Thermal imager and angle iron
-
Figure 14.
3D model of the angle iron
-
Figure 15.
Acquired thermal images
-
Figure 16.
Surface temperature field reconstruction results
-
Figure 17.
Reconstruction results (the left image of each image shows the acquired thermal image, and the right image shows the corresponding reconstruction results). (a) Replacement of background diagonal iron reconstruction at 1.2 m; (b) Reconstruction of the angular iron with the addition of interference at 1.2 m; (c) Reconstruction of a rectangular iron box placed in opposition at 0.6 m; (d) Reconstruction of an aluminium box of a horizontal cuboid at 0.5 m
-
Figure 18.
ArUco labeling and visible light camera + thermal imager solution (comparative validation tests)
-
Figure 19.
Visible and thermal maps with ArUco marking
-
Figure 20.
Reconstruction results of the poseobtained by ArUco
-
Figure 21.
Z-axis translational radar plot
- Figure .
