# A Deeply Supervised Semantic Segmentation Method Based on GAN

arXiv: https://arxiv.org/abs/2310.04081 · HTML: https://mlsanity.com/paper/2310.04081/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [Bidirectional Learning for Domain Adaptation of Semantic Segmentation](https://mlsanity.com/paper/1904.10620/) (2019)
- [Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation](https://mlsanity.com/paper/2010.05862/) (2020)
- [Universal Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1811.10323/) (2018)
- [Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks](https://mlsanity.com/paper/1903.12489/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Semantic Segmentation Algorithm for Pleural Effusion Based on DBIF-AUNet](https://mlsanity.com/paper/2508.06191/) (2025)
- [Contour Refinement using Discrete Diffusion in Low Data Regime](https://mlsanity.com/paper/2602.05880/) (2026)

## Abstract (arXiv)

In recent years, the field of intelligent transportation has witnessed rapid advancements, driven by the increasing demand for automation and efficiency in transportation systems. Traffic safety, one of the tasks integral to intelligent transport systems, requires accurately identifying and locating various road elements, such as road cracks, lanes, and traffic signs. Semantic segmentation plays a pivotal role in achieving this task, as it enables the partition of images into meaningful regions with accurate boundaries. In this study, we propose an improved semantic segmentation model that combines the strengths of adversarial learning with state-of-the-art semantic segmentation techniques. The proposed model integrates a generative adversarial network (GAN) framework into the traditional semantic segmentation model, enhancing the model's performance in capturing complex and subtle features in transportation images. The effectiveness of our approach is demonstrated by a significant boost in performance on the road crack dataset compared to the existing methods, textiti.e., SEGAN. This improvement can be attributed to the synergistic effect of adversarial learning and semantic segmentation, which leads to a more refined and accurate representation of road structures and conditions. The enhanced model not only contributes to better detection of road cracks but also to a wide range of applications in intelligent transportation, such as traffic sign recognition, vehicle detection, and lane segmentation.