Real-time human detection on FPV drones using YOLOv11 and ESP-NOW - Aria Kusumah Sastradinata; Bagus Hendra Saputra; Rifky Adishatya; Gumayang Fitri Annisa; Lusy Amelia; Belinda Zhafira; Mukhamad Ayx T Zus Rizal Tofa (Institute of Computer Science (IOCS), 2026)
Conventional aerial surveillance systems still rely heavily on human operators, which may lead to visual fatigue, limited monitoring coverage, and delayed responses during security patrol operations. This study proposes a real-time human detection system for FPV drone surveillance using the YOLOv11 object detection model integrated with ESP-NOW wireless communication. The proposed system incorporates temporal validation and human-in-the-loop confirmation to improve detection reliability and maintain operator control during response activation. Experimental evaluations were conducted under morning, afternoon, and evening conditions. The proposed system achieved average confidence values of 81.25%, 78.38%, and 79.88%, with detection success rates of 71.13%, 75.94%, and 78.03%, respectively. Furthermore, the ESP-NOW communication subsystem successfully transmitted activation signals with delays ranging from 7 ms to 53 ms and maintained stable communication over distances up to 300 m. The main contribution of this research lies in the integration of YOLOv11, temporal validation, human-in-the-loop confirmation, and ESP-NOW communication into a single UAV surveillance framework, enabling reliable real-time human detection while preserving human supervision in operational decision-making.