  Road disease detection is challenging due to the the small proportion of road
damage in target region and the diverse background,which introduce lots of
domain information.Besides, disease categories have high similarity,makes the
detection more difficult. In this paper, we propose a new LDBFSS(Latent Domain
Background Feature Separation and Suppression) network which could perform
background information separation and suppression without domain supervision
and contrastive enhancement of object features.We combine our LDBFSS network
with YOLOv5 model to enhance disease features for better road disease
detection. As the components of LDBFSS network, we first design a latent domain
discovery module and a domain adversarial learning module to obtain pseudo
domain labels through unsupervised method, guiding domain discriminator and
model to train adversarially to suppress background information. In addition,
we introduce a contrastive learning module and design k-instance contrastive
loss, optimize the disease feature representation by increasing the inter-class
distance and reducing the intra-class distance for object features. We
conducted experiments on two road disease detection datasets, GRDDC and CNRDD,
and compared with other models,which show an increase of nearly 4% on GRDDC
dataset compared with optimal model, and an increase of 4.6% on CNRDD dataset.
Experimental results prove the effectiveness and superiority of our model.
