Enhancement of Measurement Distance in Vision Sensor-Based System Using Super-Resolution for Dynamic Response Measurement Under Low-Illumination Conditions

Displacement is a key response index for evaluating the serviceability and safety of civil infrastructure. Although vision sensors have emerged as non-contact and cost-effective alternatives to conventional sensing systems, their performance deteriorates under low-illumination and long-distance conditions because reduced spatial resolution and weakened local intensity information make feature extraction and tracking unreliable. To address this limitation, this study investigates the feasibility of integrating a real-world super-resolution (SR) framework into a vision-sensor-based system for dynamic displacement measurement under low-light conditions. A GAN-based RealSR model was employed to restore low-resolution image sequences, and the restored images were processed using a KAZE-based feature tracking approach. The proposed framework was validated through shaking table tests of a three-story frame structure under a low-illumination condition of 120 lux at measurement distances ranging from 10 to 40 m. Dynamic displacement responses induced by a harmonic excitation of 9.0 Hz were estimated and compared with reference measurements obtained from a linear variable differential transformer (LVDT). The results show that the SR-restored images provided more stable feature extraction and more reliable displacement tracking than bicubic upsampling, particularly for natural targets under low-light and long-range conditions. Furthermore, the application of SR improved the measurability of targets at extended distances, demonstrating that real-world SR is an effective preprocessing strategy for robust vision-based displacement measurement under low illumination.