# Do Object Detection Localization Errors Affect Human Performance and Trust?

Source: [Do Object Detection Localization Errors Affect Human Performance and Trust?](<https://www.sota2.com/research/paper/68fa71fe1e209131cea53f8e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:16:25.405Z

Authors: Sven de Witte, Ombretta Strafforello, Jan van Gemert

Published: 2024-01-31T13:24:51.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.17821>)

[PDF](<https://arxiv.org/pdf/2401.17821.pdf>)