Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks. We investigate the relationship between bounding box localization errors and human task performance. We use observer performance studies on a visual multi-object counting task to measure both human trust and performance with different levels of bounding box accuracy. The results show that localization errors have no significant impact on human accuracy or trust in the system. Recall and precision errors impact both human performance and trust, suggesting that optimizing algorithms based on the F1 score is more beneficial in human-computer tasks. Lastly, the paper offers an improvement on bounding boxes in multi-object counting tasks with center dots, showing improved performance and better resilience to localization inaccuracy.

通过观察多目标计数任务中观察者表现研究，探究了边界框定位误差与人类任务表现之间的关系，并发现定位误差对人类准确性和信任度没有显著影响，而召回率和精确率误差则会影响人类的表现和信任度，因此优化算法时基于F1分数更有利于人机任务。最后，本文提出了在带有中心点的多目标计数任务中的边界框改进方法，展示了改进的性能和更好的对定位误差的韧性。

目标检测定位错误是否影响人类表现和信任度？