Comparative Study of YOLOv10, YOLOv11 and YOLOv12 Lightweight Models for Multi-Class Maritime Search and Rescue Using UAV Imagery
Year: 2025
Type: article
Abstract: . The maritime Search and Rescue (SAR) operation requires effective and accurate object detection systems capable of identifying various targets in dynamic sea environments and low-light situations. The paper presents a comparative study of the YOLOv10, YOLOv11, and YOLOv12 networks in multi-class marine detection using UAV images. The SeaDronesSee Odv2 dataset has been preprocessed using physics-based augmentation that mimics environmental changes, such as fog, noon, sunset, dawn, and cloudy scenarios. A multi-resolution tiling procedure was implemented to preserve the image consistency of small objects. Results show that YOLOv11s is the model that has the least accuracy-efficiency trade-off, with an [email protected] of 0.888 and an F1-Score of 0.872 at a reasonable inference time. Precision-recall analysis has shown that large maritime objects were detected with high precision, while small objects were detected with average recall. The results show that multi-resolution preprocessing, as well as physicsbased augmentation, enhance the robustness and generalization of the network. Altogether, YOLOv11s is the most stable version to use in real-time maritime SAR missions with UAVs due to its ability to handle a variety of visual conditions.
Institution: De La Salle University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7117700798