Real-time human detection on FPV drones using YOLOv11 and ESP-NOW
DOI:
https://doi.org/10.35335/cit.Vol18.2026.1609.pp133-150Keywords:
ESP-NOW, FPV Drone, Human Detection, Real-Time Detection, Temporal ValidationAbstract
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.
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References
A. Polina, H. Suparwito, and R. Kumalasanti, “Aerial object detection analysis: Challenges and preliminary results,” E3S Web Conf., vol. 475, Jan. 2024, doi: 10.1051/e3sconf/202447502017.
G. Tang, J. Ni, Y. Zhao, Y. Gu, and W. Cao, “A survey of object detection for UAVs based on deep learning,” Remote Sens., vol. 16, no. 1, p. 149, 2023.
J. Zhang, G. Wan, M. Jiang, G. Lu, X. Tao, and Z. Huang, “Small object detection in UAV image based on improved YOLOv5,” Syst. Sci. Control Eng., vol. 11, no. 1, p. 2247082, 2023.
L. Jiao and M. I. Abdullah, “YOLO series algorithms in object detection of unmanned aerial vehicles: a survey,” Serv. Oriented Comput. Appl., vol. 18, no. 3, pp. 269–298, 2024.
S.-Y. Yang, H.-Y. Cheng, and C.-C. Yu, “Real-time object detection and tracking for unmanned aerial vehicles based on convolutional neural networks,” Electronics, vol. 12, no. 24, p. 4928, 2023.
Z. Xu, H. Zhao, P. Liu, L. Wang, G. Zhang, and Y. Chai, “SRTSOD-YOLO: stronger real-time small object detection algorithm based on improved YOLO11 for UAV imageries,” Remote Sens., vol. 17, no. 20, p. 3414, 2025.
Z. Saeed, M. H. Yousaf, R. Ahmed, S. A. Velastin, and S. Viriri, “On-board small-scale object detection for unmanned aerial vehicles (UAVs),” Drones, vol. 7, no. 5, p. 310, 2023.
J. Baur and F. O. Nitsche, “A False-Positive-Centric Framework for Object Detection Disambiguation,” Remote Sens., vol. 17, no. 14, p. 2429, 2025.
S. K. Dubey, J. V Satyanarayana, and C. K. Mohan, “False positive elimination in object detection methods for videos,” in Sixteenth International Conference on Machine Vision (ICMV 2023), SPIE, 2024, pp. 146–153.
F. Lu, C. Zeng, H. Shi, Y. Xu, and S. Fu, “Real-Time Detection Sensor for Unmanned Aerial Vehicle Using an Improved YOLOv8s Algorithm,” Sensors, vol. 25, no. 19, p. 6246, 2025.
M. A. Hailan, N. M. Ghazaly, and B. M. Albaker, “ESPNow Protocol-Based IIoT System for Remotely Monitoring and Controlling Industrial Systems,” J. Robot. Control, vol. 5, no. 6, pp. 1924–1942, 2024.
F. P. Wedyanti and A. Wagyana, “Analisis Performasi ESP-NOW Sebagai Protokol Komunikasi Efisien Antar Multi-node dan Gateway dalam Sistem IoT,” in Seminar Nasional Inovasi Vokasi, 2025, pp. 607–614.
H. Herdianto, H. Hafni, D. Nasution, and S. Ramadhan, “Implementasi metode YOLO pada deteksi objek manusia,” METHOMIKA J. Manaj. Inform. Komputerisasi Akunt., vol. 8, no. 2, pp. 234–240, 2024.
P. Sharma, R. Tyagi, and P. Dubey, “Optimizing Real-Time Object Detection-A Comparison of YOLO Models.,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 12, no. 3, pp. 57–74, 2024.
R. Khanam, T. Asghar, and M. Hussain, “Comparative performance evaluation of yolov5, yolov8, and yolov11 for solar panel defect detection,” in Solar, MDPI, 2025, p. 6.
R. Sapkota, Z. Meng, M. Churuvija, X. Du, Z. Ma, and M. Karkee, “Comprehensive performance evaluation of yolov12, yolo11, yolov10, yolov9 and yolov8 on detecting and counting fruitlet in complex orchard environments,” arXiv Prepr. arXiv2407.12040, 2024.
E. Mosqueira-Rey, E. Hernández-Pereira, D. Alonso-Ríos, J. Bobes-Bascarán, and Á. Fernández-Leal, “Human-in-the-loop machine learning: a state of the art,” Artif. Intell. Rev., vol. 56, no. 4, pp. 3005–3054, 2023.
L. Zhu, J. Xiong, F. Xiong, H. Hu, and Z. Jiang, “Yolo-drone: Airborne real-time detection of dense small objects from high-altitude perspective,” arXiv Prepr. arXiv2304.06925, 2023.
J. Nassar, V. Pavon-Harr, M. Bosch, and I. McCulloh, “Assessing data quality of annotations with Krippendorff alpha for applications in computer vision,” arXiv Prepr. arXiv1912.10107, 2019.
U. Nisa, “Image augmentation approaches for small and tiny object detection in aerial images: A review,” Multimed. Tools Appl., vol. 84, no. 19, pp. 21521–21568, 2025.
X. Hao, L. Liu, R. Yang, L. Yin, L. Zhang, and X. Li, “A review of data augmentation methods of remote sensing image target recognition,” Remote Sens., vol. 15, no. 3, p. 827, 2023.
A. M. Abdulghani, M. M. Abdulghani, W. L. Walters, and K. H. Abed, “Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm.,” J. Artif. Intell., vol. 5, 2023.
D. Cai, Z. Zhang, and Z. Zhang, “Corner-point and foreground-area IoU loss: Better localization of small objects in bounding box regression,” Sensors, vol. 23, no. 10, p. 4961, 2023.
X. Qian, S. Gao, W. Deng, and W. Wang, “Improving oriented object detection by scene classification and task-aligned focal loss,” Mathematics, vol. 12, no. 9, p. 1343, 2024.
Y. Ding et al., “Application of Improved YOLOv8 Image Model in Urban Manhole Cover Defect Management and Detection: Case Study,” Sensors, vol. 25, no. 13, p. 4144, 2025.
M. Liu, C. Zhang, and C. Lin, “GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish,” Front. Mar. Sci., vol. 12, p. 1574580, 2025.
A. A. Adegun, J. V. Fonou Dombeu, S. Viriri, and J. Odindi, “State-of-the-art deep learning methods for objects detection in remote sensing satellite images,” Sensors, vol. 23, no. 13, p. 5849, 2023.
L. He, Y. Zhou, L. Liu, W. Cao, and J. Ma, “Research on object detection and recognition in remote sensing images based on YOLOv11,” Sci. Rep., vol. 15, no. 1, p. 14032, 2025.
A. Rahman, Y. Lu, and H. Wang, “Performance evaluation of deep learning object detectors for weed detection for cotton,” Smart Agric. Technol., vol. 3, p. 100126, 2023.
Y. Ma et al., “I-YOLOv11n: A Lightweight and Efficient Small Target Detection Framework for UAV Aerial Images,” Sensors, vol. 25, no. 15, p. 4857, 2025.
L. Xu, Y. Zhao, Y. Zhai, L. Huang, and C. Ruan, “Small object detection in UAV images based on Yolov8n,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, p. 223, 2024.
Z. Yuan et al., “Small object detection in uav remote sensing images based on intra-group multi-scale fusion attention and adaptive weighted feature fusion mechanism,” Remote Sens., vol. 16, no. 22, p. 4265, 2024.
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Copyright (c) 2026 Aria Kusumah Sastradinata, Bagus Hendra Saputra, Rifky Adishatya, Gumayang Fitri Annisa, Lusy Amelia, Belinda Zhafira, Mukhamad Ayx T Zus Rizal Tofa
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