Object detectors in real-world applications often fail to detect objects due to varying factors such as weather conditions and noisy input. Therefore, a process that mitigates false detections is crucial for both safety and accuracy. While uncertainty-based thresholding shows promise, previous works demonstrate an imperfect correlation between uncertainty and detection errors. This hinders ideal thresholding, prompting us to further investigate the correlation and associated cost with different types of uncertainty. We therefore propose a cost-sensitive framework for object detection tailored to user-defined budgets on the two types of errors, missing and false detections. We derive minimum thresholding requirements to prevent performance degradation and define metrics to assess the applicability of uncertainty for failure recognition. Furthermore, we automate and optimize the thresholding process to maximize the failure recognition rate w.r.t. the specified budget. Evaluation on three autonomous driving datasets demonstrates that our approach significantly enhances safety, particularly in challenging scenarios. Leveraging localization aleatoric uncertainty and softmax-based entropy only, our method boosts the failure recognition rate by 36-60\% compared to conventional approaches. Code is available at https://mos-ks.github.io/publications.

为了提高对象检测的准确性和安全性，研究提出了一种基于阈值和不确定性的成本敏感的对象检测框架，用于避免错误检测。通过分析不同类型的不确定性与检测错误之间的相关性和成本，该方法自动优化阈值以最大化对指定预算的错误识别率，在自动驾驶数据集上的评估结果表明，该方法较传统方法提高了36-60%的错误识别率。

面向成本敏感的基于不确定性的物体检测故障识别