# ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet

Source: [ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet](<https://www.sota2.com/research/paper/68fa762a1e209131cea6b9f9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:48:32.506Z

Authors: Andrei-Robert Alexandrescu, Razvan-Gabriel Petec, Alexandru Manole, Laura-Silvia Diosan

Published: 2024-12-09T18:34:49.000Z

## Abstract

Deep Learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand of data required to achieve the potential of this type of model. An ever-increasing sub-field of Artificial Intelligence, Image Synthesis, aims to address this limitation through the design of intelligent models capable of creating original and realistic images, endeavour which could drastically reduce the need for real data. The Stable Diffusion generation paradigm recently propelled state-of-the-art approaches to exceed all previous benchmarks. In this work, we propose the ContRail framework based on the novel Stable Diffusion model ControlNet, which we empower through a multi-modal conditioning method. We experiment with the task of synthetic railway image generation, where we improve the performance in rail-specific tasks, such as rail semantic segmentation by enriching the dataset with realistic synthetic images.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.06742>)

[PDF](<https://arxiv.org/pdf/2412.06742.pdf>)