## 2 Materials and Methods
### Related Work
The study used a limited amount of data, with only 435 images being used for training and testing. Out of the total data, 300 images were used for pollution detection and 135 images were used for non-pollution detection. Image segmentation with a fully convolutional network was used to identify pollution regions. Segmented regions were classified as pollution/non-pollution using SVM. The optical flow between the frames was calculated to estimate the movement direction of the polluted regions. Aerial images of a real ship oil spill off the coast of Taiwan were used for testing. Segmentation plus SVM achieved 99.95% accuracy in detecting oil-polluted regions. The optical flow visualization demonstrated the capability to track the spread of oil spills. Limitations include a small training dataset size and limited pollution types. The study showed potential for automated marine monitoring and oil spill response using AI and drones. Paper [15] proposed a novel technique to quantify riverine plastic debris based on unmanned aerial vehicles (UAVs), specifically focusing on Malaysia's Klang River. Visual counting measurements were taken during fieldwork between 29 April and 4 May 2019, from the Jalan Tengku Kalana bridge, while UAV-borne measurements were collected downstream at 700 m. Comparing these results with traditional methods revealed that UAV-based monitoring offers an alternative to the monitoring of riverine plastic transport, particularly in inaccessible and remote locations. A related study [7] outlined an innovative methodology that employs drones and deep learning techniques to detect and map marine litter in coastal regions. The authors emphasized the severity of the marine litter issue, particularly plastics, and underscored the limitations of existing monitoring methods in terms of coverage and efficiency. The researchers enlisted commercial off-the-shelf drones operated by citizens to gather aerial images of beaches. These images were then segmented into tiles and annotated by volunteers with classifications such as \"litter\" or \"no litter\" utilizing an online platform, ultimately generating training data. Subsequently, five deep convolutional neural network architectures were trained on these annotated tiles to autonomously identify marine litter. A total of 1975 ultra-resolution images (5472 \(\times\) 3648 pixels with an aspect ratio of 3:2) were captured using the DJI Phantom 4 Pro v2 quadcopter. The Phantom, with a 20-megapixel camera and a mechanical shutter, was mounted to a three-axis gimbal. The raw images were divided into smaller tiles, each measuring 512 \(\times\) 512 pixels, and were annotated using the Zooniverse tool. The authors divided the tiles into two classes, namely, litter and not litter. The proposed citizen science protocol using commercial drones and crowdsourced image labeling enabled the efficient collection of a large training dataset of over 30,000 labeled image tiles. The drone metadata extraction process allowed the georeferencing and spatial analysis of results. The VGG19 architecture achieved the best performance with 77.6% accuracy on the held-out beach test set for litter detection. The density maps visualized the concentration and distribution patterns of litter reasonably well compared to manual surveys. The deep learning model litter detection results aligned with manual visual classifications with an R2 of 0.97 between density maps. A similar study [8] put forth a pragmatic approach for the automatic mapping of plastic waste in rivers by combining the use of UAVs and deep learning models. This method has the potential to benefit a diverse array of stakeholders involved in monitoring and mitigating plastic pollution in waterways. By leveraging UAVs and deep learning models, the precise detection of plastic litter in rivers--including rural areas and transboundary rivers--becomes practical and efficient.