HyEff-UNet: an attention-driven Efficientnet U-Net with hybrid loss optimization for UAV-based marine oil spill detection and segmentation

Introduction Monitoring oil spills in the port area is very difficult and can spread rapidly, resulting in considerable damage to the environment. This study proposes HyEff-UNet, a deep learning-based semantic segmentation model for the detection of oil spills using drone-captured RGB images of port environments. Methods HyEff-UNet employs a modified version of the U-Net model with an EfficientNet-B4 model as an encoder is used as the model architecture to classify the pixels of the input image as oil, water, or other surface regions. A combined categorical cross-entropy and Dice loss function is used to jointly optimize pixel-wise classification and region overlap. Results The results show that the complete architecture achieves the highest performance among the internally evaluated models, while the sequential ablation indicates that the largest performance improvement is observed after the hybrid categorical cross-entropy and Dice loss is introduced following the EfficientNet-B4 and attention-based architecture. Conclusion These findings demonstrate the effectiveness of the complete architecture within the evaluated configurations. However, the sequential ablation study does not isolate the independent contribution of the hybrid loss. Furthermore, the measured inference latency indicates that further optimization and unmanned aerial vehicle (UAV)-specific benchmarking are required before real-time onboard deployment can be considered.