Uncertainty Quantification for Object Detection: Output- and Gradient-Based Approaches

Abstract

Safety-critical applications of deep neural networks require reliable confidence estimation methods with high predictive power. However, evaluating and comparing different methods for uncertainty quantification is oftentimes highly context-dependent. In this chapter, we introduce flexible evaluation protocols which are applicable to a wide range of tasks with an emphasis on object detection. In this light, we investigate uncertainty metrics based on the network output, as well as metrics based on a learning gradient, both of which significantly outperform the confidence score of the network. While output-based uncertainty is produced by post-processing steps and is computationally efficient, gradient-based uncertainty, in principle, allows for localization of uncertainty within the network architecture. We show that both sources of uncertainty are mutually non-redundant and can be combined beneficially. Furthermore, we show direct applications of uncertainty quantification by improving detection accuracy.

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety

“…The problem of realistically quantifying uncertainty measures will be taken up in Chapter "Uncertainty Quantification for Object Detection: Output-and Gradient-based Approaches" [RSKR22]. Here output-based and learning-gradient-based uncertainty metrics for object detection will be examined, showing that they are non-correlated.…”