论文标题
用于对象检测的多元置信度校准
Multivariate Confidence Calibration for Object Detection
论文作者
论文摘要
神经网络的公正置信度估计是至关重要的，特别是对于安全至关重要的应用。已经开发了许多方法来校准偏见的置信度估计值。尽管有多种分类方法，但尚未解决对象检测的领域。因此，我们提出了一个新型框架，以测量和校准对象检测方法的偏置（或错误校准）置信度估计。分类器校准领域中相关工作的主要区别在于，我们还使用对象检测器的回归输出的其他信息进行校准。我们的方法首次允许对图像位置和盒子比例获得校准的置信度估计。此外，我们提出了一项新措施来评估对象探测器的错误校准。最后，我们表明，我们开发的方法优于对象检测任务的最先进校准模型，并在不同位置和尺度上提供了可靠的置信度估计。
Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods. The main difference to related work in the field of classifier calibration is that we also use additional information of the regression output of an object detector for calibration. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. In addition, we propose a new measure to evaluate miscalibration of object detectors. Finally, we show that our developed methods outperform state-of-the-art calibration models for the task of object detection and provides reliable confidence estimates across different locations and scales.
