UAV Multispectral Imaging for Oil and Water Leak Detection

Pipeline leakages involving oil and water pose significant environmental and economic risks, motivating the need for timely and reliable surface leak detection techniques. This paper presents a proof-of-concept study that investigates the use of UAV-based multispectral imagery and deep learning for detecting and distinguishing surface oil and water leaks over soil backgrounds. Multispectral data were acquired using a UAV-mounted sensor under controlled experimental conditions and processed through a radiometric and geometric correction pipeline. Spectral analysis was conducted to assess band-wise separability, highlighting the effectiveness of Red and Red-Edge bands for oil–water discrimination. Multiple object detection models, including YOLOv11, RT-DETR, Faster R-CNN, and a fusion-based YOLOv11-RGBT variant, were evaluated using these inputs. Experimental results demonstrate that single-band models achieve robust detection performance, with YOLOv11 attaining detection accuracies of 92.8% for oil and 98.1% for water, while RT-DETR and Faster R-CNN demonstrated comparable accuracy. The fusion-based YOLOv11-RGBT model achieved lower performance, likely due to limited dataset size for effective cross-band feature learning. Overall, the findings demonstrate that UAV-based multispectral sensing combined with deep learning offers a practical solution for localized surface leakage monitoring.