Perception serves as a critical component in the functionality of autonomous agents. However, the intricate relationship between perception metrics and robotic metrics remains unclear, leading to ambiguity in the development and fine-tuning of perception algorithms. In this paper, we introduce a methodology for quantifying this relationship, taking into account factors such as detection rate, detection quality, and latency. Furthermore, we introduce two novel metrics for Human-Robot Collaboration safety predicated upon perception metrics: Critical Collision Probability (CCP) and Average Collision Probability (ACP). To validate the utility of these metrics in facilitating algorithm development and tuning, we develop an attentive processing strategy that focuses exclusively on key input features. This approach significantly reduces computational time while preserving a similar level of accuracy. Experimental results indicate that the implementation of this strategy in an object detector leads to a maximum reduction of 30.091% in inference time and 26.534% in total time per frame. Additionally, the strategy lowers the CCP and ACP in a baseline model by 11.252% and 13.501%, respectively. The source code will be made publicly available in the final proof version of the manuscript.

翻译：感知作为自主智能体功能中的关键组成部分。然而，感知度量与机器人度量之间的复杂关系尚不明确，导致感知算法的开发与调优存在歧义。本文提出了一种量化这种关系的方法，综合考虑检测率、检测质量及延迟等因素。此外，我们引入了两种基于感知度量的人机协作安全性新指标：临界碰撞概率（CCP）与平均碰撞概率（ACP）。为验证这些指标在促进算法开发与调优中的实用性，我们设计了一种专注于关键输入特征的注意力处理策略。该方法在保持相似精度的同时显著降低了计算时间。实验结果表明，将该策略应用于目标检测器后，每帧推理时间最大减少30.091%，总时间最大减少26.534%。此外，该策略使基线模型的CCP和ACP分别降低了11.252%和13.501%。源代码将在论文最终定稿版本中公开。