A Method for Correcting Temperature-Induced Errors in Vision-Based Measurement Systems

Zhang Qiang; Hu Biao; Wang Yongwei; Liang Huiping; Chen Wenjun; Yin Yihe; Liu Xiaolin; Liang Zhanyu; Huang Qiahao; Yu Qifeng

doi:10.3788/gzxb20265507.0712005

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A Method for Correcting Temperature-Induced Errors in Vision-Based Measurement Systems

更新时间：2026-06-12

- A Method for Correcting Temperature-Induced Errors in Vision-Based Measurement Systems
- Acta Photonica Sinica Pages: 1-16(2026)
- 作者机构：
  
  1.深圳市智能光测与感知重点实验室，深圳 518060
  2.澳门大学科技学院，澳门特别行政区999078
  3.深圳大学物理与光电工程学院，深圳 518060
  4.中交第二航务工程局有限公司，武汉 430048
- 作者简介：
- 基金信息：
  
  the National Natural Science Foundation of China(52208399);the Shenzhen Research Start-up Fund for Newly Recruited High-caliber and Urgently Needed Talents
- DOI：10.3788/gzxb20265507.0712005
  CLC： TP391
- CSTR：32255.14.gzxb20265507.0712005
- Received：14 February 2026，
  
  Revised：2026-05-15，
  
  Accepted：18 May 2026，
- 稿件说明：
移动端阅览
张强，胡彪，王永威，等. 视觉测量相机温致误差的在线修正方法［J］.光子学报，2026，55（7）：0712005

Zhang Qiang， Hu Biao， Wang Yongwei， et al. A Method for Correcting Temperature-Induced Errors in Vision-Based Measurement Systems［J］. Acta Photonica Sinica， 2026， 55（7）：0712005
张强，胡彪，王永威，等. 视觉测量相机温致误差的在线修正方法［J］.光子学报，2026，55（7）：0712005 DOI： 10.3788/gzxb20265507.0712005. CSTR： 32255.14.gzxb20265507.0712005.

Zhang Qiang， Hu Biao， Wang Yongwei， et al. A Method for Correcting Temperature-Induced Errors in Vision-Based Measurement Systems［J］. Acta Photonica Sinica， 2026， 55（7）：0712005 DOI： 10.3788/gzxb20265507.0712005. CSTR： 32255.14.gzxb20265507.0712005.

摘要

在视觉测量系统长期运行条件下，相机自身发热以及环境温度波动会引起相机成像模型变化，进而显著降低测量系统的稳定性与精度。本文提出了一种基于双通道共视成像系统的相机温致误差在线修正方法。该方法采用紧凑的双通道光路结构，使测量点与校准点同时成像于同一图像平面；在此基础上，构建基于针孔成像模型的简化像点漂移方程，实现温致误差的在线补偿。通过搭建原型系统并开展室内动态温度实验与室外长期监测实验，对所提方法的有效性进行了验证。实验结果表明，在室内动态温度变化条件下，温致误差修正后像点漂移精度优于0.30pixel；在室外长期监测场景中，位移修正误差优于0.50pixel，相较于未修正前精度提升最高可达90.2%。与传统离线标定方法不同，所提方法仅需完成系统初始标定，在共视成像结构保持不变的条件下，可直接适用于不同型号相机，无需重新构建温度—参数映射关系。所提方法适用于野外工程结构的长期、无人值守位移监测及其他对温度稳定性要求较高的视觉测量应用场景。

Abstract

Temperature variation in long-term vision-based measurement systems can cause time-varying drift of camera projection parameters， leading to image-point instability and degradation of measurement accuracy. To address this problem， an online correction method for temperature-induced errors is proposed based on a dual-channel co-view imaging system. The method is designed to suppress thermally induced image-point drift without relying on external temperature sensors or pre-established temperature-parameter mapping databases.A compact dual-channel co-view imaging system is constructed by optically coupling a calibration plane and a measurement plane onto the same image sensor through a beam-splitting module. Since the two channels share the same imaging lens， sensor， and principal optical path， temperature-induced perturbations can be treated as highly correlated within a unified imaging model. Based on the pinhole projection model， a simplified image-point drift model is established by introducing small perturbations to the intrinsic and extrinsic parameters. Through parameter coupling analysis， the original model is reduced to a seven-parameter linear form， which is solved online by least squares using the pixel drift of multiple calibration feature points. The estimated parameter vector is then directly substituted into the image-point drift model to calculate the thermally induced drift of measurement points， enabling point-wise correction of distorted image coordinates and recovery of in-plane displacement on the measurement plane.The effectiveness of the proposed method is validated through controlled indoor thermal cycling experiments and outdoor long-term monitoring tests. Indoor experiments simulate dynamic temperature variations within a temperature-controlled chamber， while outdoor experiments evaluate system robustness under real environmental fluctuations and known displacement inputs. Indoor experiments demonstrate that camera intrinsic parameters， including principal point and equivalent focal length， exhibit repeatable periodic drift under temperature cycling. After applying the proposed online correction strategy， residual image-point fluctuations in both horizontal and vertical directions are reduced to less than 0.2 pixels， and the overall drift accuracy is better than 0.30 pixels. Statistical evaluation using RMSE， MAE， and peak error indicators shows that global image-point drift is reduced by more than 87%， with consistent suppression across both image axes.In outdoor long-duration experiments， the system maintains stable performance under significant ambient temperature variations. For static measurement points， displacement drift induced by temperature changes is effectively suppressed， with corrected errors controlled within 2 mm. For controlled displacement tests using a precision translation stage， the maximum displacement error is reduced from 1.01 mm to 0.42 mm in the X direction and from 5.42 mm to 0.78 mm in the Y direction， corresponding to a maximum improvement of 90.2%. These results confirm that the proposed approach remains effective under non-steady thermal conditions.The proposed dual-channel co-view online correction strategy enables real-time estimation and compensation of thermally induced projection parameter drift without external temperature sensing or offline recalibration， significantly enhancing the stability， robustness， and engineering applicability of long-term vision-based displacement monitoring systems under complex thermal environments.

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references

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