论文标题
不结盟但安全 - 正式补偿不精确2D对象检测的性能限制
Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection
论文作者
论文摘要
在本文中，我们考虑基于机器学习的2D对象检测及其对安全性的影响。我们讨论了性能限制的特殊子类型：预测边界框不能与地面真相完全保持一致，但是计算出的与工会的相交始终大于给定的阈值。在这种类型的绩效限制下，我们正式证明了覆盖地面真相的最低限制框扩大因子。然后，我们证明该因素可以数学上调整为较小的值，前提是运动计划者在做出决策时采用固定长度缓冲区。最后，观察经验测量的扩大因子与我们正式得出的最坏情况扩大因子之间的差异在定量证据（统计证明）和定性证据（通过最差分析证明）之间提供了有趣的联系。
In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: the prediction bounding box cannot be perfectly aligned with the ground truth, but the computed Intersection-over-Union metric is always larger than a given threshold. Under such type of performance limitation, we formally prove the minimum required bounding box enlargement factor to cover the ground truth. We then demonstrate that the factor can be mathematically adjusted to a smaller value, provided that the motion planner takes a fixed-length buffer in making its decisions. Finally, observing the difference between an empirically measured enlargement factor and our formally derived worst-case enlargement factor offers an interesting connection between the quantitative evidence (demonstrated by statistics) and the qualitative evidence (demonstrated by worst-case analysis).
