Do Object Detection Localization Errors Affect Human Performance and Trust?
This work addresses the problem of designing effective human-computer interfaces for object detection, providing insights for developers to prioritize algorithm improvements, though it is incremental in nature.
The study investigated whether bounding box localization errors affect human performance and trust in object detection tasks, finding no significant impact, while recall and precision errors did affect both, suggesting F1 score optimization is more beneficial.
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.