The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a growing demand for highly reliable models in these domains, making the problem of uncertainty calibration pivotal, when considering the future of deep learning. This is especially true when considering object detection systems, that are commonly present in safety-critical application such as autonomous driving and robotics. For this reason, this work presents a novel theoretical and practical framework to evaluate object detection systems in the context of uncertainty calibration. The robustness of the proposed uncertainty calibration metrics is shown through a series of representative experiments. Code for the proposed uncertainty calibration metrics at: https://github.com/pedrormconde/Uncertainty_Calibration_Object_Detection.

翻译：深度神经网络的普及使得机器学习系统越来越多地应用于各类实际场景。因此，这些领域对高可靠性模型的需求日益增长，使得不确定性校准问题在深度学习未来发展中至关重要。这一需求在目标检测系统中尤为突出，此类系统常见于自动驾驶和机器人等安全关键应用。为此，本文提出一种新颖的理论与实践框架，用于评估目标检测系统在不确定性校准方面的表现。通过一系列代表性实验，展示了所提出的不确定性校准指标的鲁棒性。所提出的不确定性校准指标代码详见：https://github.com/pedrormconde/Uncertainty_Calibration_Object_Detection。