Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection

arXiv:2309.06747v2 Announce Type: replace
Abstract: 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 …

abstract adversarial arxiv cs.cv current data detection domain driving gan generate generative generative adversarial networks maintenance networks prior safety synthesis texture traffic traffic safety type