Abstract
Oil spills in the Niger Delta pose a severe environmental threat, yet existing detection methods remain slow, manual and unsuitable for large-scale continuous monitoring. While existing studies have applied individual YOLOv8 variants to oil spill detection, no prior study has systematically benchmarked YOLOv8n, YOLOv8s and YOLOv8m under identical conditions framed around Niger Delta deployment constraints. This study benchmarks three YOLOv8 variants nano, small and medium trained and evaluated under identical conditions. The dataset used is a publicly available Oil Spill Detection dataset from Roboflow Universe, comprising 2,567 images across four classes: object, rainbow, sheen and truecolor. The models were evaluated on a held-out test set of 72 images using mAP@50, mAP@50-95, Precision, Recall and Inference Speed. It was found that YOLOv8m achieved the highest accuracy at 81.95% mAP@50, while YOLOv8n achieved the fastest inference at 6.2ms with 68.66% mAP@50, and YOLOv8s offered no clear advantage over nano. YOLOv8n is recommended for resource-constrained deployment given its speed advantage, while YOLOv8m is recommended where computational resources allow, given its superior accuracy. These findings show that deep learning based detection is a viable and practical approach for automated environmental monitoring in the Niger Delta region, offering a better pathway toward localized, scalable oil spill surveillance in a region that has lacked such tools, helping guide faster cleanup response.