Real-Time Human Detection Using YOLOv11 for Early Warning in Beach Safety Zones
Received: 27 September 2025 | Revised: 14 December 2025 and 17 January 2026 | Accepted: 19 January 2026 | Online: 4 April 2026
Corresponding author: Kusrini
Abstract
This study proposes a novel approach for beach safety monitoring by implementing a real-time human object detection system using the state-of-the-art YOLOv11 model. The main objective is to detect human subjects—specifically their heads and shoulders—and classify their locations into predefined safety zones: safe, caution, and danger. The system is designed to trigger an early warning protocol when individuals are identified within the danger zone, providing a crucial intervention window. The study utilizes a meticulously pre-processed custom dataset, which has been tailored for this specific application. The performance of the YOLOv11 model is then quantitatively evaluated using standard metrics, including Precision, Recall, and mean Average Precision @ 0.50-0.95 Intersection over Union (mAP@0.50-0.95 IoU). The findings demonstrate the feasibility of employing deep learning models for proactive risk management, with the proposed system achieving a mAP@0.50-0.95 of 67.6% and an impressive inference speed of 2.1 ms per image. The study offers a scalable and efficient solution to enhance visitor safety and prevent potential accidents in dynamic beach environments.
Keywords:
maritime surveillance, YOLOv11, artificial intelligenceDownloads
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Copyright (c) 2026 Kusrini, Bayu Setiaji, Elik Hari Muktafin, Anna Baita, Bima Pramudya Asaddulloh, Dwi Ahmad Dzulhijjah, Faris Khairil Murtadho
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