  Damage to road pavement can develop into cracks, potholes, spallings, and
other issues posing significant challenges to the integrity, safety, and
durability of the road structure. Detecting and monitoring the evolution of
these damages is crucial for maintaining the condition and structural health of
road infrastructure. In recent years, researchers have explored various
data-driven methods for image-based damage detection in road monitoring
applications. The field gained attention with the introduction of the Road
Damage Detection Challenge (RDDC2018), encouraging competition in developing
object detectors on street-view images from various countries. Leading teams
have demonstrated the effectiveness of ensemble models, mostly based on the
YOLO and Faster R-CNN series. Data augmentations have also shown benefits in
object detection within the computer vision field, including transformations
such as random flipping, cropping, cutting out patches, as well as
cut-and-pasting object instances. Applying cut-and-paste augmentation to road
damages appears to be a promising approach to increase data diversity. However,
the standard cut-and-paste technique, which involves sampling an object
instance from a random image and pasting it at a random location onto the
target image, has demonstrated limited effectiveness for road damage detection.
This method overlooks the location of the road and disregards the difference in
perspective between the sampled damage and the target image, resulting in
unrealistic augmented images. In this work, we propose an improved
Cut-and-Paste augmentation technique that is both content-aware (i.e. considers
the true location of the road in the image) and perspective-aware (i.e. takes
into account the difference in perspective between the injected damage and the
target image).
