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.

该研究介绍了一种新的 LDBFSS（潜在领域背景特征分离和抑制）网络，它能够在没有领域监督的情况下执行背景信息的分离和抑制，并通过对目标特征进行对比增强以改善道路疾病检测；实验证明该模型在道路疾病检测数据集上取得了显著的增强效果。

基于潜在域背景特征分离和抑制的道路病害检测

Road Disease Detection based on Latent Domain Background Feature  Separation and Suppression

The impression is crucial for the referring physicians to grasp key
information since it is concluded from the findings and reasoning of
radiologists. To alleviate the workload of radiologists and reduce repetitive
human labor in impression writing, many researchers have focused on automatic
impression generation. However, recent works on this task mainly summarize the
corresponding findings and pay less attention to the radiology images. In
clinical, radiographs can provide more detailed valuable observations to
enhance radiologists' impression writing, especially for complicated cases.
Besides, each sentence in findings usually focuses on single anatomy, so they
only need to be matched to corresponding anatomical regions instead of the
whole image, which is beneficial for textual and visual features alignment.
Therefore, we propose a novel anatomy-enhanced multimodal model to promote
impression generation. In detail, we first construct a set of rules to extract
anatomies and put these prompts into each sentence to highlight anatomy
characteristics. Then, two separate encoders are applied to extract features
from the radiograph and findings. Afterward, we utilize a contrastive learning
module to align these two representations at the overall level and use a
co-attention to fuse them at the sentence level with the help of
anatomy-enhanced sentence representation. Finally, the decoder takes the fused
information as the input to generate impressions. The experimental results on
two benchmark datasets confirm the effectiveness of the proposed method, which
achieves state-of-the-art results.

本文提出了一种新的解决方案来增强放射学家的结论写作，重点在于，将放射学图像和检查结果组合在一起生成结论，主要包括解剖学增强多模态模型和对比学习模块，经过两个基准数据集的实验证明，该方法显著优于其他自动结论生成模型。