We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. State-of-the-art architectures are tested and the most promising one undergoes the process of conformalization, where a correction is applied to the predicted bounding boxes (i.e. to their height and width) such that they comply with a predefined probability of success. We work with a novel exploratory dataset of images taken from the perspective of a train operator, as a first step to build and validate future trustworthy machine learning models for the detection of railway signals.

翻译：我们提出将共形预测（一种具有保证的不确定性量化方法）应用于铁路信号检测。本文测试了当前最先进的架构，并对其中最具潜力的模型进行共形化处理——通过对预测边界框（即其高度和宽度）进行校正，使其满足预定义的成功概率。我们使用从列车驾驶员视角采集的新型探索性图像数据集开展工作，这是构建和验证未来可信机器学习模型以用于铁路信号检测的第一步。