Real-Time Marine Oil Spill Detection Using YOLO-Based Object Detectors
TL;DR
Findings indicate that real-time oil spill monitoring should be assessed jointly on accuracy, strict localization and compute cost, rather than ranked using mAP@50 alone.
Abstract
Detecting oil spills at sea is a difficult vision problem. Slicks typically have soft edges, scatter into irregular patches, and resemble several harmless features of the sea surface. This paper presents a controlled benchmark of six YOLO-based object detectors applied to this task, namely YOLOv8n, YOLOv8s, YOLO11n, YOLO11s, YOLO26n and YOLO26s. Every model was trained and evaluated under strictly identical conditions, using an annotated oil spill dataset and the AdamW optimizer. To provide a deployment-oriented evaluation, we report not only detection accuracy but localization robustness and computational cost through precision, recall, F1-score, mAP@50, mAP@50:95, inference time, FPS, model size, number of parameters, GFLOPs and training time. A single accuracy-efficiency score is introduced to help practitioners choose a model that fits real operational constraints. In the experiments, YOLOv8s delivers the strongest localization, reaching 0.9824 mAP@50 and 0.8825 mAP@50:95, together with the highest recall (0.9647). YOLO11s leads on precision and F1 -score, with values of 0.9814 and 0.9593, respectively. In contrast YOLOv8n offers the most suitable deployment profile, with 6.09ms inference time, 164.08 FPS and the best overall accuracy--efficiency score. Together, these findings indicate that real-time oil spill monitoring should be assessed jointly on accuracy, strict localization and compute cost, rather than ranked using mAP@50 alone.