Oil Spill Detection in UAV-based Environmental Monitoring Using Lightweight Deep Learning Architectures

Abstract

This paper presents the development and evaluation of a lightweight onboard deep learning module for real-time oil spill detection within a distributed UAV-based maritime environmental monitoring system. The study investigates four object detection architectures under controlled training conditions using both baseline and augmented dataset configurations: three convolutional one-stage detectors (YOLOv8s, YOLO11s, YOLO26s) and one transformer-based model (RF-DETR Nano). Performance was assessed using precision, recall, mAP@50, mAP@50–95, and inference latency on an NVIDIA Tesla T4 GPU. Experimental results show that RF-DETR Nano achieves the highest localization accuracy (mAP@50–95 = 0.863) while maintaining real-time throughput (≈ 50 FPS), whereas YOLO26s provides the most favourable efficiency–accuracy trade-off among CNN-based models (mAP@50–95 = 0.806 at ≈ 67 FPS). The findings demonstrate that accurate and computationally efficient oil spill detection can be performed directly onboard UAV platforms, enabling reliable autonomous maritime monitoring without dependence on continuous ground-based processing.