  In the domain of traffic safety and road maintenance, precise detection of
road damage is crucial for ensuring safe driving and prolonging road
durability. However, current methods often fall short due to limited data.
Prior attempts have used Generative Adversarial Networks to generate damage
with diverse shapes and manually integrate it into appropriate positions.
However, the problem has not been well explored and is faced with two
challenges. First, they only enrich the location and shape of damage while
neglect the diversity of severity levels, and the realism still needs further
improvement. Second, they require a significant amount of manual effort. To
address these challenges, we propose an innovative approach. In addition to
using GAN to generate damage with various shapes, we further employ texture
synthesis techniques to extract road textures. These two elements are then
mixed with different weights, allowing us to control the severity of the
synthesized damage, which are then embedded back into the original images via
Poisson blending. Our method ensures both richness of damage severity and a
better alignment with the background. To save labor costs, we leverage
structural similarity for automated sample selection during embedding. Each
augmented data of an original image contains versions with varying severity
levels. We implement a straightforward screening strategy to mitigate
distribution drift. Experiments are conducted on a public road damage dataset.
The proposed method not only eliminates the need for manual labor but also
achieves remarkable enhancements, improving the mAP by 4.1% and the F1-score by
4.5%.
