关键词: 链式机器视觉, 结构变形, 误差传递, 精度评估

Abstract: [Purposes] This paper aims to address the issues of unclear error accumulation mechanisms caused by multi-station serial connections and the difficulty in predicting accuracy under long-distance conditions in tunnel chain-type machine vision deformation monitoring systems. [Methods] This paper constructs a refined chained visual error propagation model that considers the station-by-station propagation and coupling of multi-source errors.It analyzes the comprehensive impact mechanism of calibration residual errors，sub-pixel positioning noise，environmental disturbances，and registration errors of overlapping points between adjacent measurement stations on the accuracy of remote measurement points，and simultaneously adopts the Topcon MS05AX total station for automated monitoring and accuracy evaluation. [Findings] Experimental results show that the internal consistency accuracy of chain-based machine vision is superior to 0.30 mm in both horizontal and vertical directions.The STD value generally shows an upward trend with increasing distance，and the overall repeatability in the vertical direction is slightly better than that in the horizontal direction.The RMS value in the horizontal direction is 1.04 mm，and the RMS value in the vertical direction is 1.13 mm.The predictive ability of the proposed error model for the attenuation trend of far-end accuracy is verified，revealing the inherent mechanism that vertical repeatability is superior to horizontal repeatability，and environmental disturbances are the main contributors to errors at long distances. [Conclusions] The error propagation model established in this paper can effectively explain the accuracy degradation pattern of the chain-based vision system in tunnel environments.The overall accuracy of the system can meet the requirements for monitoring structural deformations in subways，but the cumulative effect of errors over long distances is significant.It is recommended to adopt an error suppression strategy that combines regular joint calibration with redundant benchmark adjustments．

Key words: chain-based machine vision, structural deformation, error propagation, accuracy evaluation