Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forensic applications where the system cannot be trained for a specific acquisition device. Predictions on such out-of-distribution images have an increased chance of failing. But this failure case is oftentimes hard to recognize for a human operator or an automated system. Hence, in this work we propose to model the prediction uncertainty for license plate recognition explicitly. Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition. In this paper, we compare three methods for uncertainty quantification on two architectures. The experiments on synthetic noisy or blurred low-resolution images show that the predictive uncertainty reliably finds wrong predictions. We also show that a multi-task combination of classification and super-resolution improves the recognition performance by 109\% and the detection of wrong predictions by 29 %.

翻译：基于学习的自动车牌识别算法隐含假设训练数据和测试数据高度一致，但在极端环境条件或无法针对特定采集设备训练系统的法医应用中，这一假设可能不成立。此类分布外图像的预测失败概率更高，而人工操作员或自动化系统往往难以识别这种失败情形。因此，本研究明确提出了车牌识别的预测不确定性建模方法。这种不确定性度量能够检测错误预测，向分析人员指示何时不应信任自动车牌识别结果。本文比较了两种架构上的三种不确定性量化方法。在合成噪声或模糊低分辨率图像上的实验表明，预测不确定性能够可靠地发现错误预测。我们还证明，分类与超分辨率的多任务组合可将识别性能提升109%，并将错误预测检测率提升29%。