Presentation
Seeing What Matters: A Systematic Review of Aerial Person Detection for Drone-Based Search and Rescue
DescriptionDrones are increasingly used in search and rescue (SAR) missions to provide rapid, real-time situation awareness in complex environments. However, drone operators process vast amounts of visual data under time-sensitive conditions, often on constrained displays, creating significant cognitive demand. Object detection algorithms offer a promising solution by identifying and highlighting potential human targets in aerial imagery. Although prior reviews have focused heavily on the technical optimization of these algorithms, far less attention has been given to how these systems support human operators in practice. This systematic review synthesizes 67 studies published between 2010 to 2025 to examine the role of object detection algorithms in drone-based SAR from a human factors perspective. Findings reveal that evaluations overwhelmingly emphasize technical performance measures, with minimal assessment of operator workload, situation awareness, or team performance. Additionally, most studies rely on static datasets, limiting their relevance to real-world SAR operations. Across studies, human limitations were consistently cited as primary motivations for developing object detection systems, yet seldom translated into measurable outcomes. The findings reveal a significant disconnect between technical innovation and human-centered evaluation, highlighting the need for research that integrates core human factors constructs before these systems can be effectively deployed in real-world SAR missions.
Event Type
Lecture
TimeThursday, October 22nd3:20pm - 3:40pm PDT
Location
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