# Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling

arXiv: https://arxiv.org/abs/2304.06052 · HTML: https://mlsanity.com/paper/2304.06052/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [Conformal Risk Control](https://mlsanity.com/paper/2208.02814/) (2022)
- [Distribution-Free, Risk-Controlling Prediction Sets](https://mlsanity.com/paper/2101.02703/) (2021)
- [Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control](https://mlsanity.com/paper/2110.01052/) (2021)
- [Predictive inference with the jackknife+](https://mlsanity.com/paper/1905.02928/) (2019)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)
- [A comparison of some conformal quantile regression methods](https://mlsanity.com/paper/1909.05433/) (2019)
- [BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors](https://mlsanity.com/paper/1903.03838/) (2019)
- [PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction](https://mlsanity.com/paper/2001.00106/) (2019)
- [Probabilistic Object Detection: Definition and Evaluation](https://mlsanity.com/paper/1811.10800/) (2018)
- [Monte Carlo DropBlock for Modelling Uncertainty in Object Detection](https://mlsanity.com/paper/2108.03614/) (2021)
- [FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains](https://mlsanity.com/paper/2002.05665/) (2020)
- [Towards PAC Multi-Object Detection and Tracking](https://mlsanity.com/paper/2204.07482/) (2022)
- [Conformal Prediction for Trustworthy Detection of Railway Signals](https://mlsanity.com/paper/2301.11136/) (2023)

## Built on by (most cited first, 22 of 22)

- [Conformal Prediction for Image Segmentation Using Morphological Prediction Sets](https://mlsanity.com/paper/2503.05618/) (2025)
- [Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework](https://mlsanity.com/paper/2503.08308/) (2025)
- [Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data](https://mlsanity.com/paper/2310.09926/) (2023)
- [Conformal Structured Prediction](https://mlsanity.com/paper/2410.06296/) (2024)
- [Max-Rank: Efficient Multiple Testing for Conformal Prediction](https://mlsanity.com/paper/2311.10900/) (2023)
- [Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation](https://mlsanity.com/paper/2503.13947/) (2025)
- [Conformal confidence sets for biomedical image segmentation](https://mlsanity.com/paper/2410.03406/) (2024)
- [Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP](https://mlsanity.com/paper/2505.16740/) (2025)
- [Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems](https://mlsanity.com/paper/2408.07828/) (2024)
- [Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks](https://mlsanity.com/paper/2506.05434/) (2025)
- [The Penalized Inverse Probability Measure for Conformal Classification](https://mlsanity.com/paper/2406.08884/) (2024)
- [Conformal Object Detection by Sequential Risk Control](https://mlsanity.com/paper/2505.24038/) (2025)
- [Conformal Risk Control for Pulmonary Nodule Detection](https://mlsanity.com/paper/2412.20167/) (2024)
- [Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees](https://mlsanity.com/paper/2504.17721/) (2025)
- [TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection](https://mlsanity.com/paper/2609.08659/) (2026)
- [Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction](https://mlsanity.com/paper/2501.07185/) (2025)
- [Efficient Precision Control in Object Detection Models for Enhanced and Reliable Ovarian Follicle Counting](https://mlsanity.com/paper/2501.14036/) (2025)
- [Conformal Prediction Sets for Instance Segmentation](https://mlsanity.com/paper/2602.10045/) (2026)
- [Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning](https://mlsanity.com/paper/2510.18485/) (2025)
- [Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing](https://mlsanity.com/paper/2604.26411/) (2026)
- [Probabilistic Object Detection with Conformal Prediction](https://mlsanity.com/paper/2605.07549/) (2026)
- [Conformal Coverage Guarantees for Any Video Temporal Grounder](https://mlsanity.com/paper/2608.07434/) (2026)

## Abstract (arXiv)

Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we demonstrate the use of the conformal prediction framework to construct reliable and trustworthy predictors for detecting railway signals. Our approach is based on a novel dataset that includes images taken from the perspective of a train operator and state-of-the-art object detectors. We test several conformal approaches and introduce a new method based on conformal risk control. Our findings demonstrate the potential of the conformal prediction framework to evaluate model performance and provide practical guidance for achieving formally guaranteed uncertainty bounds.