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.

翻译：在实际的认证系统中部署深度学习模型需要能够提供准确反映不确定性的置信度估计。本文展示了利用自抗扰预测框架构建可靠和可信任的铁路信号检测器的应用。我们采用了一个包括从列车驾驶员视角拍摄的图像和最先进的对象检测器的新型数据集。我们测试了几种自抗扰方法，并引入了一种基于置信风险控制的新方法。我们的发现展示了自抗扰预测框架评估模型性能的潜力，并提供了实现正式保证不确定性界限的实用指导。