Abstract
Detecting people from unmanned aerial vehicles (UAVs) in coastal environments is challenging because targets are often small, distant, and observed against complex backgrounds. This case study presents an application-driven system-level comparison of frame- and event-based sensing vision systems. We developed a purpose-built multimodal UAV dataset of beach users, bathers, and surfers using frame and event-based cameras. Frame images and events were evaluated using feasible standard and converted spiking YOLOv2-derived configurations on a Jetson Orin Nano and a BrainChip AKD1000 neuromorphic processor. The experimental setup serves as a challenging stress test to expose limitations and relative behavior across configurations. The evaluation covers end-to-end processing performance and energy and sensor power. Frame-based YOLOv2 achieved the highest detection performance, with an F1-score of 0.215 and an mAP50 of 0.212. The best spiking configuration, sYOLOv2 with event input, achieved an F1-score of 0.172 and an mAP50 of 0.114. Frame-based YOLOv2 on the Jetson GPU also achieved the lowest end-to-end processing energy cost (2886.6 mJ per sample) and highest throughput (2.011 samples/s). At the component level, the event camera reduced operating power by 82.1% relative to the frame camera, while the AKD1000 consumed approximately 1.3 W during backend execution. However, these component-level savings did not yield an end-to-end energy advantage because of host-processing overhead and lower throughput. The results highlight the need to assess sensing and complete-pipeline costs when evaluating the efficiency of neuromorphic embedded vision systems.