# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 1 Introduction  
Structural health monitoring (SHM) is critical for tracking the performance and condition of engineering structures over their service life, essential across mechanical, aviation, civil, and infrastructure sectors [1, 2, 3]. In light of the widely accepted assumption that local damage alters modal parameters, a variety of vibration-based damage detection methods have emerged, such as natural frequency-based [4, 5, 6], modal damping-based [7, 8, 9], and mode shape-based [10, 11, 12]. Although the frequency shift indicates alterations in the state-of-health of structures, its properties of lumped parameter restrict its application to damage localization. Additionally, practical challenges persist in obtaining modal damping. As an alternative, the rich spatial dynamic information of the mode shape-based method renders it highly suitable for the issue of detecting damage [13, 14]. Detecting irregularities in mode shapes of damaged structures enables precise damage localization. The measurement of mode shapes is the crucial step in the application of mode shape-based methods, exerting a direct influence on the accuracy of damage localization. Currently, ways to acquire mode shapes mainly fall into two categories: contact and non-contact methods. Contact methods necessitate the mounting of sensors, such as accelerometersand strain gauges, on the structure's surface for vibration signal capture [15; 16; 17; 18]. Although contact sensors are generally reliable, they cause undesired changes to the dynamics of lightweight structures, leading to mass-loading effects [19]. Moreover, conventional contact approaches provide only sparse and discrete monitoring points, resulting in low spatial measurement resolution, which is typically inadequate for mode shape-based damage detection [20]. It should also be recognized that long-term monitoring of large-scale structures using contact methods is a time-consuming and labor-intensive task. In contrast, non-contact vibration measurement techniques like the scanning laser vibrometer (SLV) and vision-based methods offer high-spatial-resolution measurements without sensors attached to the structures. [PERSON] et al. [21] introduced a damage detection method that integrates multiple indices and validated it using mode shapes captured by SLV. [PERSON] et al. [22] used SLV to measure mode shapes and mapped them into the difference of Gaussian space for damage detection. Nevertheless, such devices are costly and tend to be time-consuming when performing high-resolution spatial measurements due to the need for sequential operation [23; 24]. As an alternative, vision-based methods provide cost-effective and efficient high-spatial-resolution measurements [25; 26]. [PERSON] et al. [27] proposed a framework based on the digital image correlation (DIC) technique for cable force determination aided by mode shapes. [PERSON] et al. [28] used the three-dimensional point tracking (PT) technique to extract full-field mode shapes of a wind turbine rotor. Despite the growing research on vision-based SHM [29; 30; 31], noteworthy insufficiencies persist. Existing vision-based methods, such as DIC and PT, rely excessively on high-quality speckle patterns or high-contrast markers on the surface. This necessitates meticulous surface preparation [32; 33], posing difficulties for application on inaccessible or large structures. Furthermore, vision-based methods are susceptible to measurement noise due to the influence of image resolution and the inherent constraints of the optoelectronic system, thus impacting the precision of damage detection. The aim of this research is to propose a novel vision-based damage detection method that integrates multi-scale signal analysis with data fusion algorithms. The method employs an advanced computer vision algorithm named phase-based optical flow estimation to arrange virtual sensors across the structure. Notably, optical flow represents the motion pattern of objects within an image sequence over time.