## 4 Results  
### OS-Net Performance  
Table 1 shows the accuracy, precision, recall, and F-1 score for three models: (A) OS-Net Without Transfer Learning; (B) OS-Net With Transfer Learning Feature Extraction Strategy; (C) OS-Net With Transfer Learning Fine-tuning Strategy. In model development, we use grid search for hyperparameter tuning for each of the three models mentioned above. Then, we obtain the best hyperparameter settings and the corresponding best model performance (Table 1) for each of the models. The best initial learning rate of the best OS-Net without  
Figure 4: VOS-Net filter performance demonstration. Screening out single false-positive events improves the model accuracy to 99%. transfer learning is 0.002, which is larger than the best initial learning rate of the OS-Net with ImageNet pretrained model (0.001). We also use a learning rate schedule to adjust the learning rate during training. The learning rate will decrease rapidly in the first few epochs, then gradually reduce as the iteration continues to make the model more stable. The settings of grid search values for hyperparameters tuning in each model are listed in Table S3. The best hyperparameters for our best OS-Net model are listed here: the momentum factor is 0.9, the initial learning rate is 0.001, epochs are 30, and the batch size is 16. From the results presented in Table 1, the OS-Net With Transfer Learning and the Fine-tuning Strategy approach performed best. Therefore, we selected OS-Net With Transfer Learning and the Fine-tuning Strategy as our final OS-Net. The OS-Net is robust for the oil sheet classification task in our laboratory-generated dataset, accurately predicting the oil sheen appearance on the water surface in our OS-SS prototype. The accuracy and F-1 score of the OS-Net are up to 99%. The model can efficiently extract the low-level features and high-level features from images. The recall rate is up to 100%, which means that there are no false negatives. The reason why OS-Net Without Transfer Learning has relatively low accuracy, compared to the final OS-Net, may be a result of the relatively small size of our oil sheen image dataset. When we trained the model from scratch on our oil sheen image dataset, the knowledge that the model can learn is limited, leading to overfitting and a lack of generalizability. The model can only fit well with similar data but not data the model has never seen before. Here, we also tried a \"warm-up\" training approach to reduce variance in the early stage of training and achieve better performance for the OS-Net without transfer learning model, but the final accuracy was not improved. This result indicates the role of transfer learning is more than just \"warming up\" the weights better. In contrast, the OS-Net With Transfer Learning and the Fine-tuning Strategy is pre-trained on the ImageNet dataset, which has millions of images, providing the model with a better generalization ability. The model was then fine tuned on the specific oil sheen dataset to adjust its parameters to improve the specific oil sheen prediction task. This suggests that transfer learning is helpful to transfer the extracted basic features from ImageNet to our new task. Even though the high-level features have large visual differences between the two tasks (ImageNet has no oil sheen images), the basic feature (low-level patterns, e.g., edges, color patterns, elementary shapes) extraction ability is shared across tasks. It is worth noting that the performance of the OS-Net With Transfer Learning Feature Extraction Strategy did not improve significantly compared to the OS-Net Without Transfer Learning. This may be due to the large gap between the target (close-proximity oil sheen) images and the pre-trained model's ImageNet dataset. The types of advanced features (middle or high level) that need to be extracted from the two datasets are not the same, so using the pre-trained model as the feature extractor may not be ideal. Figure 5 shows sample images with the ground truth label and the OS-Net prediction results.