# Ultra-Fast Adaptive Track Detection Network

arXiv: https://arxiv.org/abs/2405.13538 · HTML: https://mlsanity.com/paper/2405.13538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 22 May 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [FcaNet: Frequency Channel Attention Networks](https://mlsanity.com/paper/2012.11879/) (2020)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021)
- [Rethinking Efficient Lane Detection via Curve Modeling](https://mlsanity.com/paper/2203.02431/) (2022)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [A Keypoint-based Global Association Network for Lane Detection](https://mlsanity.com/paper/2204.07335/) (2022)
- [Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification](https://mlsanity.com/paper/2206.07389/) (2022)
- [Rail Detection: An Efficient Row-based Network and A New Benchmark](https://mlsanity.com/paper/2304.05667/) (2023)
- [DALNet: A Rail Detection Network Based on Dynamic Anchor Line](https://mlsanity.com/paper/2308.11381/) (2023)
- [Train Ego-Path Detection on Railway Tracks Using End-to-End Deep Learning](https://mlsanity.com/paper/2403.13094/) (2024)

## Abstract (arXiv)

Railway detection is critical for the automation of railway systems. Existing models often prioritize either speed or accuracy, but achieving both remains a challenge. To address the limitations of presetting anchor groups that struggle with varying track proportions from different camera angles, an ultra-fast adaptive track detection network is proposed in this paper. This network comprises a backbone network and two specialized branches (Horizontal Coordinate Locator and Perspective Identifier). The Perspective Identifier selects the suitable anchor group from preset anchor groups, thereby determining the row coordinates of the railway track. Subsequently, the Horizontal Coordinate Locator provides row classification results based on multiple preset anchor groups. Then, utilizing the results from the Perspective Identifier, it generates the column coordinates of the railway track. This network is evaluated on multiple datasets, with the lightweight version achieving an F1 score of 98.68% on the SRail dataset and a detection rate of up to 473 FPS. Compared to the SOTA, the proposed model is competitive in both speed and accuracy. The dataset and code are available at https://github.com/idnihai/UFATD