Simple and contactless methods for determining the health of metallic and composite structures are necessary
to allow non-invasive Non-Destructive Evaluation (NDE) of damaged structures. Many recognized damage
detection techniques, such as frequency shift, generalized fractal dimension and wavelet transform, have been
described with the aim to identify, locate damage and determine the severity of damage. These techniques are
often tailored for factors such as (i) type of material, (ii) damage patterns (crack, impact damage, delamination),
and (iii) nature of input signals (space and time). In this paper, a wavelet-based damage detection framework
that locates damage on cantilevered composite beams via NDE using computer vision technologies is presented.
Two types of damage have been investigated in this research: (i) defects induced by removing material to reduce
stiffness in a metallic beam and (ii) manufactured delaminations in a composite laminate. The novelty in the
proposed approach is the use of bespoke computer vision algorithms for the contactless acquisition of modal
shapes, a task that is commonly regarded as a barrier to practical damage detection. Using the proposed
method, it is demonstrated that modal shapes of cantilever beams can be readily reconstructed by extracting
markers using Hough Transform from images captured using conventional slow motion cameras. This avoids
the need to use expensive equipment such as laser doppler vibrometers. The extracted modal shapes are then
used as input for a wavelet transform damage detection, exploiting both discrete and continuous variants. The
experimental results are verified using finite element models (FEM).