Print Email Facebook Twitter Do Object Detection Localization Errors Affect Human Performance and Trust? Title Do Object Detection Localization Errors Affect Human Performance and Trust?: An Observer Performance Study Author de Witte, Sven (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics) Contributor van Gemert, J.C. (mentor) Strafforello, O. (mentor) Martinez, Jorge (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2023-12-21 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 localizationinaccuracy. Subject Observer PerformanceObject DetectionBounding BoxTrust To reference this document use: http://resolver.tudelft.nl/uuid:d7f02e67-7d9e-484d-bfc2-2cfc069bc35f Part of collection Student theses Document type master thesis Rights © 2023 Sven de Witte Files PDF Thesis_Sven_de_witte_2023_FINAL.pdf 12.59 MB Close viewer /islandora/object/uuid:d7f02e67-7d9e-484d-bfc2-2cfc069bc35f/datastream/OBJ/view