让目标检测模型学会评估自身预测的不确定性，提升可靠性。
An Uncertainty-aware DETR Enhancement Framework for Object Detection
- 将边界框建模为多变量高斯分布，用分布距离优化损失函数。
- 在COCO上显著提升检测精度，且在白细胞检测任务中达领先水平。
- 适合需要可靠预测的医学图像、自动驾驶等关键场景。
本文聚焦于目标检测中的定位精度与预测不确定性建模问题。传统检测器采用确定性边界框回归，忽略预测不确定性，限制了模型鲁棒性。本文提出一种面向DETR的不确定性感知增强框架，将边界框建模为多变量高斯分布，并引入格罗莫夫-沃瑟斯坦距离作为损失函数，以更好对齐预测分布与真实分布。在此基础上，我们推导出贝叶斯风险公式用于过滤高风险信息，提升检测可靠性。此外，提出一种简单算法，通过置信区间量化定位不确定性。在COCO基准上的实验表明，该方法可有效集成至现有DETR变体，显著提升性能。进一步扩展至白细胞检测任务，在LISC和WBCDD数据集上取得当前最优结果。这些结果验证了该框架在通用与特定领域检测任务中的可扩展性。
原文摘要 · Abstract (English)
This paper investigates the problem of object detection with a focus on improving both the localization accuracy of bounding boxes and explicitly modeling prediction uncertainty. Conventional detectors rely on deterministic bounding box regression, ignoring uncertainty in predictions and limiting model robustness. In this paper, we propose an uncertainty-aware enhancement framework for DETR-based object detectors. We model bounding boxes as multivariate Gaussian distributions and incorporate the Gromov-Wasserstein distance into the loss function to better align the predicted and ground-truth distributions. Building on this, we derive a Bayes Risk formulation to filter high-risk information and improve detection reliability. We also propose a simple algorithm to quantify localization uncertainty via confidence intervals. Experiments on the COCO benchmark show that our method can be effectively integrated into existing DETR variants, enhancing their performance. We further extend our framework to leukocyte detection tasks, achieving state-of-the-art results on the LISC and WBCDD datasets. These results confirm the scalability of our framework across both general and domain-specific detection tasks. Code page: https://github.com/ParadiseforAndaChen/An-Uncertainty-aware-DETR-Enhancement-Framework-for-Object-Detection.
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