In our contemporary world, environmental issues and the ever-present threat of plastic pollution endanger not only the health of our planet but also that of its inhabitants, underscoring the urgency of action. The need to closely monitor these destructive phenomena and develop effective detection systems is imperative to preserve our fragile ecosystem. Fortunately, the emergence of cutting-edge technologies has revolutionized our ability to monitor and detect environmental threats with unprecedented precision. The combined use of drones and artificial intelligence, particularly deep learning, yields promising results, leveraging drones' unique capabilities to cover vast areas and the power of deep learning to analyze collected data swiftly and accurately. Our study focuses on optimizing the utilization of drones and object detection algorithms through deep learning for effective detection and supervision of plastic litter. We will explore the performance of two major families of object detection models, namely single-pass and double-pass, using drone images captured at varying heights. The overarching objective is to identify the optimal performance-to-resource conditions, maximizing efficiency in our detection and supervision endeavors. This research is crucial in addressing the pressing environmental concerns posed by plastic pollution, offering innovative solutions to mitigate its impact and safeguard the health of our planet for future generations.
YANDOUZI et al. (Sat,) studied this question.