SDPPA

Plastic pollution of aquatic environments is a critical problem in today's world. According to UN reports from 2021, around 14 million tons of plastic waste enters aquatic environments every year. The slow decomposition of these in water generates microplastics, which directly affect water quality. Although there are patrol measures in place to identify the accumulation of plastic waste in these environments, there is a lack of methods that automate and facilitate the efficient monitoring of these areas, both for environmental protection purposes and for the collection of geo-environmental data. Therefore, the aim of this project is to develop and "train" software based on artificial intelligence with the ability to detect and locate plastic waste in images of aquatic environments. To this end, prior to programming the proposed system, an image bank was created for training purposes, containing various photos of plastic waste, as well as an image bank aimed at tests depicting aquatic environments subjected to plastic pollution. Programming the software, which was done in Python, begins with pre-processing the input images, which are converted into shades of gray and slightly blurred by a low-pass filter before being subjected to the Sobel Filter, used for edge detection. The result of this process undergoes a dilation operation, which improves the connectivity of the contours, making them more apparent. After this, a bounding box is drawn around each object detected from the contours, taking into account its position in the image matrix. A separate image is generated for each object, with its boundaries determined by the bounding box. These new images are analyzed by artificial intelligence. The model was trained using the transfer learning method to acquire basic segmentation and feature extraction knowledge from a pre-existing model. It was then subjected to supervised learning to learn the graphic characteristics that define plastic waste, based on manually identified examples. In the identification routine, if the artificial intelligence classifies the object as plastic, the box delimiting the waste is highlighted in the output image; otherwise, the box is ignored. Image processing was made possible with the help of the OpenCV library, and machine training with the TenserFlow library. Tests on the system were carried out continuously for as long as it was programmed, since there is a hierarchy of task execution. At the end of this process, we tested the software with several images, which showed satisfactory results in identifying various types of plastic waste in images of aquatic environments, showing that the research objective has been achieved so far. However, the system is currently limited to daytime images, due to the absence of nighttime images in the test image bank. Future steps in the project include adapting the software developed to operate in real time, so that it can be implemented in aquatic environment monitoring systems.