# RailYolact -- A Yolact Focused on edge for Real-Time Rail Segmentation

arXiv: https://arxiv.org/abs/2410.09612 · HTML: https://mlsanity.com/paper/2410.09612/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Deep Watershed Transform for Instance Segmentation](https://mlsanity.com/paper/1611.08303/) (2016)
- [InstanceCut: from Edges to Instances with MultiCut](https://mlsanity.com/paper/1611.08272/) (2016)
- [Pixelwise Instance Segmentation with a Dynamically Instantiated Network](https://mlsanity.com/paper/1704.02386/) (2017)
- [Faster Training of Mask R-CNN by Focusing on Instance Boundaries](https://mlsanity.com/paper/1809.07069/) (2018)

## Abstract (arXiv)

Ensuring obstacle avoidance on the rail surface is crucial for the safety of autonomous driving trains and its first step is to segment the regions of the rail. We chose to build upon Yolact for our work. To address the issue of rough edge in the rail masks predicted by the model, we incorporated the edge information extracted by edge operator into the original Yolact's loss function to emphasize the model's focus on rail edges. Additionally, we applied box filter to smooth the jagged ground truth mask edges cause by linear interpolation. Since the integration of edge information and smooth process only occurred during the training process, the inference speed of the model remained unaffected. The experiments results on our custom rail dataset demonstrated an improvement in the prediction accuracy. Moreover, the results on Cityscapes showed a 4.1 and 4.6 improvement in AP and AP50 , respectively, compared to Yolact.