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

arXiv: https://arxiv.org/abs/2309.07616 · HTML: https://mlsanity.com/paper/2309.07616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 61 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 29 of 29)

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- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deep Learning Face Representation by Joint Identification-Verification](https://mlsanity.com/paper/1406.4773/) (2014)
- [Decoupled Contrastive Learning](https://mlsanity.com/paper/2110.06848/) (2021)
- [Targeted Supervised Contrastive Learning for Long-Tailed Recognition](https://mlsanity.com/paper/2111.13998/) (2021)
- [A Closer Look at Smoothness in Domain Adversarial Training](https://mlsanity.com/paper/2206.08213/) (2022)
- [Maximum Density Divergence for Domain Adaptation](https://mlsanity.com/paper/2004.12615/) (2020)
- [Rethinking Minimal Sufficient Representation in Contrastive Learning](https://mlsanity.com/paper/2203.07004/) (2022)
- [Dissecting Supervised Contrastive Learning](https://mlsanity.com/paper/2102.08817/) (2021)
- [Robust Contrastive Learning against Noisy Views](https://mlsanity.com/paper/2201.04309/) (2022)
- [Model Agnostic Sample Reweighting for Out-of-Distribution Learning](https://mlsanity.com/paper/2301.09819/) (2023)
- [Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning](https://mlsanity.com/paper/2204.07596/) (2022)
- [Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques](https://mlsanity.com/paper/2006.16471/) (2020)
- [Domain Generalization by Learning and Removing Domain-specific Features](https://mlsanity.com/paper/2212.07101/) (2022)
- [Global Road Damage Detection: State-of-the-art Solutions](https://mlsanity.com/paper/2011.08740/) (2020)
- [Road Damage Detection and Classification with Detectron2 and Faster R-CNN](https://mlsanity.com/paper/2010.15021/) (2020)
- [An Efficient and Scalable Deep Learning Approach for Road Damage Detection](https://mlsanity.com/paper/2011.09577/) (2020)
- [Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis](https://mlsanity.com/paper/2010.10681/) (2020)
- [Road Damage Detection using Deep Ensemble Learning](https://mlsanity.com/paper/2011.00728/) (2020)
- [FasterRCNN Monitoring of Road Damages: Competition and Deployment](https://mlsanity.com/paper/2010.11780/) (2020)
- [CNN Model & Tuning for Global Road Damage Detection](https://mlsanity.com/paper/2103.09512/) (2021)

## Abstract (arXiv)

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.