A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking system

Abstract

Structural displacement is an imperative indicator for safety evaluation and maintenance. To address the limitations of conventional displacement sensors, advanced non-contact vision-based trackers offer a promising alternative. Based on the reconstructed Efficient Convolution Operator (ECO), a more powerful multi-resolution deep feature framework is proposed to efficiently encode the informative representation. The fusion of the shallow convolutional and the deep convolutional features is discriminative while preserving spatial and structural information. Furthermore, the discrete feature map is transferred to the continuous spatial domain by introducing an interpolation operator to achieve accurate sub-pixel registration. A careful comparison of the results on the steel suspension bridge demonstrates the high accuracy of the multi-resolution deep feature tracker (MDFT) for displacement measurement. The performances in the time and frequency domain show decent agreement with the results acquired by the laser displacement sensor (LDS), which confirm the low-cost, target-free, high resolution, and non-contact measurement capacities.