This article presents a novel drone-based method for high-precision displacement measurement of bridges, achieving sub-millimeter accuracy. The approach utilizes phase information from drone-captured images and integrates a bio-inspired strategy to account for camera motion-induced displacements. By employing a group of strategic reference markers on bridge girders, the method effectively isolates structural displacements from camera movement. The technique combines phase-based sampling moiré with four degrees-of-freedom geometric modeling to accurately determine bridge deflections. The system achieves a precision of 1/100th of a pixel, demonstrating high accuracy and reliability. Real-world validations confirm the method's effectiveness, making it a practical tool for bridge displacement measurement. The research highlights the potential of this methodology in advancing autonomous infrastructure inspection systems. The study addresses the challenges of aging infrastructure, emphasizing the need for efficient and cost-effective non-destructive evaluation methods. Traditional sensing devices face limitations in field applications, while vision-based methods, such as digital image correlation and phase-based techniques, offer promising solutions. The proposed drone-based system overcomes these limitations by leveraging the maneuverability of unmanned aerial vehicles (UAVs) to achieve high-precision measurements. The method was tested on a 110-meter-long concrete bridge, demonstrating accuracy comparable to conventional deflection measurement sensors. The results show that the system can measure deflections with a precision of 0.2 mm, with discrepancies between the proposed method and conventional sensors being less than 1 mm. The study also validates the method on a 35-meter-long bridge, achieving an average error of 0.199 mm. The proposed approach offers significant advantages, including high accuracy, low computational complexity, and robustness to pixel noise. The method's potential for future autonomous bridge inspection systems is highlighted, with the ability to reduce human intervention and improve efficiency. The research underscores the importance of addressing aging infrastructure challenges through innovative, cost-effective solutions. The study also acknowledges the limitations of the method, including the dependency on marker pitch for displacement accuracy and the suitability for bridges with stationary girders. The findings demonstrate the feasibility of using drone-based photography for accurate bridge displacement measurement, offering a practical solution to the challenges of aging infrastructure.This article presents a novel drone-based method for high-precision displacement measurement of bridges, achieving sub-millimeter accuracy. The approach utilizes phase information from drone-captured images and integrates a bio-inspired strategy to account for camera motion-induced displacements. By employing a group of strategic reference markers on bridge girders, the method effectively isolates structural displacements from camera movement. The technique combines phase-based sampling moiré with four degrees-of-freedom geometric modeling to accurately determine bridge deflections. The system achieves a precision of 1/100th of a pixel, demonstrating high accuracy and reliability. Real-world validations confirm the method's effectiveness, making it a practical tool for bridge displacement measurement. The research highlights the potential of this methodology in advancing autonomous infrastructure inspection systems. The study addresses the challenges of aging infrastructure, emphasizing the need for efficient and cost-effective non-destructive evaluation methods. Traditional sensing devices face limitations in field applications, while vision-based methods, such as digital image correlation and phase-based techniques, offer promising solutions. The proposed drone-based system overcomes these limitations by leveraging the maneuverability of unmanned aerial vehicles (UAVs) to achieve high-precision measurements. The method was tested on a 110-meter-long concrete bridge, demonstrating accuracy comparable to conventional deflection measurement sensors. The results show that the system can measure deflections with a precision of 0.2 mm, with discrepancies between the proposed method and conventional sensors being less than 1 mm. The study also validates the method on a 35-meter-long bridge, achieving an average error of 0.199 mm. The proposed approach offers significant advantages, including high accuracy, low computational complexity, and robustness to pixel noise. The method's potential for future autonomous bridge inspection systems is highlighted, with the ability to reduce human intervention and improve efficiency. The research underscores the importance of addressing aging infrastructure challenges through innovative, cost-effective solutions. The study also acknowledges the limitations of the method, including the dependency on marker pitch for displacement accuracy and the suitability for bridges with stationary girders. The findings demonstrate the feasibility of using drone-based photography for accurate bridge displacement measurement, offering a practical solution to the challenges of aging infrastructure.