Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 316-338
Abstract
This independent study presents a system for object detection and localization using aerial imagery captured by drones in search and rescue operations. Generally, higher drone altitude gives greater area coverage, but reduces detection accuracy. While a lower altitude improves accuracy, but requires more search time. Lacking guidance on optimal altitude information, this study explores the various detection performances at different flight altitudes to enhance operational efficiency. Since altitude impacts both image quality and detection accuracy, image resolution is also examined as a key factor in system performance. The study evaluates the YOLOv11 algorithm for detection in aerial images, using clothing as a human proxy to address ethical and data collection constraints. Performance was assessed using Mean Average Precision, Precision, Recall, and Time along with, derived metrics like Efficiency Score and Missing Rate. The geolocation deviation is also measured. Findings indicated that increasing altitude reduces model performance but can be compensated by using a higher resolution image. For missions requiring high detection accuracy, the lowest altitude flights yield the best results. In contrast, more time-constrained operations can benefit from higher altitude but need more computation resources. In general, the study suggests a flight altitude of 40 meters with 1080×720 resolution as the most efficient altitude. At 40 meters, detection accuracy slightly decreases, but area coverage and computation speed improve significantly by roughly three times with the top Efficiency Score and lowest Missing Rate.