## 5 Discussion and Conclusions  
This paper introduces the concept of a subaquatic sediment oil sheen screening system (OS-SS) and develops a novel oil sheen detection system to autonomously monitor the sediment's sheening potential after disturbance with compressed air or water. The OS-SS also provides reliable real-time oil sheen video. Based on the videos from OS-SS, we successfully created a new close-proximity oil sheen image dataset, which includes thousands of oil sheen images (five types of oil sheen formation details under different environmental conditions in the lab). This dataset can become a resource for a broad range of oil-sheen-modeling-related research. We developed an Oil Sheen Prediction Neural Network (OS-Net), a Convolutional Neural Network (CNN), combined with transfer learning, to achieve real-time image-based oil sheen prediction from video and applied the model in a simulation of the OS-SS. The accuracy of OS-Net was up to 99%. To mitigate false positives from the video stream, we employed a sensitivity filter in the VOS-Net. Thus, our VOS-Net can output accurate feedback to monitor the formation of an oil sheen present given a video stream, even if that formation is short lived in the order of a few seconds to a few tens of seconds. The kernel size, a hyperparameter-insensitive filter, can be adjusted to control the sensitivity of VOS-Net to avoid false positives. Moreover, we determined the image extraction frequency parameters in VOS-Net needed to avoid model prediction latency and provide real-time feedback. To further reduce the model reference time and extend the range of image extraction frequency, we tried to use depth-wise convolution to replace the regular convolution in the OS-Net to reduce the number of parameters and computations. As a result, compared to the original OS-Net, the accuracy of the OS-Net model with depth-wise convolution was reduced by 1.66% and the average latency was reduced by 2.65%. The result suggests that depth-wise convolution can reduce the inference time. How to use depth-wise convolution to reduce model latency while also ensuring accuracy will be a good question to explore in the future. Model quantization, mapping values from a larger set to a smaller one to reduce the floating point, is another approach worth trying to reduce the memory and complexity of computations in the future. The OS-SS with VOS-Net could be easily applied in the field, potentially being deployed on an autonomous watercraft to map the spatial distribution of sediment sheening potential in rivers, creeks, fords, and shallow waters [43]. The VOS-Net can automatically show the real-time result and record the detection result along with the GPS location to determine the precise location of the sediments with high sheening potential. The OS-SS with VOS-Net could be an efficient tool for mapping problematic regions of subaquatic sediments for further evaluation or remediation. The autonomous nature of the approach also makes it ideal for deployment in difficult-to-reach terrains. Moreover, as our OS-Net is based on visible light images, the requirements and costs for image collection equipment are low. The algorithms could be further explored to be used with a cellphone camera to detect the oil sheen on the water or in other similar scenarios, such as oil drilling or storm runoff in the future. Furthermore, our results indicated that combining residual blocks with transfer learning helped our OS-Net to overcome the challenge of limited datasets to obtain a deeper and more robust neural network and achieve high accuracy. The appropriate transfer learning strategy was related to the pre-training and task datasets. When there is a significant gap between the two datasets, a fine-tuning transfer learning strategy improved the accuracy of the model. The improved performance of OS-Net is a good example. Data augmentation can also potentially enhance the training dataset for the small dataset. In our case, using random horizontal flip alone improved the model's accuracy by 0.65% (SI Table S2).