Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the -- essentially hand-crafted -- non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration; this makes it parallelizable with no hyper-parameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The \hyperlink{https://github.com/Blueblue4/IoU-AwareCalibration}{code} for all our experiments is publicly available.

翻译：目标检测器是许多半自主和全自主决策系统的核心，并且有望变得更加不可或缺。然而，它们在可访问性方面仍然存在不足，有时会产生不可靠的预测。尤其令人担忧的是——本质上为手工设计的——非极大值抑制算法，它导致了预测过程的模糊化和有偏的置信度估计。我们证明，通过使用IoU感知校准，可以消除经典的NMS式后处理。IoU感知校准是一种条件Beta校准，无需超参数即可实现并行化。它并非采用任意截断或折扣，而是隐式地考虑每个检测是重复检测的可能性，并相应调整置信度分数，从而为每个检测提供基于经验数据的精度估计。我们在不同检测架构上进行的大量实验表明，所提出的IoU感知校准能够成功地对重复检测进行建模并改善校准效果。与标准的顺序式NMS加校准方法相比，我们的联合建模能够在性能上超越最优的基于NMS的替代方案，同时以更低的复杂度持续生成校准更好的置信度预测。所有实验的\hyperlink{https://github.com/Blueblue4/IoU-AwareCalibration}{代码}均已公开。