ADVANCES IN MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR PLASTIC LITTER DETECTION IN MARINE ENVIRONMENTS

Abstract

A serious threat to the environment is plastic pollution in marine ecosystems, and thus an effective detection of litter plastics is needed for proper management. This review critically assesses recent studies that use CNNs and other machine learning approaches to detect and measure plastic debris in various water bodies. The study delves into the models, datasets, and evaluation measures used in these studies factoring in persistent challenges associated with detecting small objects and variability of environmental conditions. In addition, the study offers future perspectives highlighting the need for complete data gathering, utilization of various sources of imagery, and development of real-time monitoring mechanisms to combat plastic pollution. Through the integration of these findings, this review attempts to assist researchers, decision-makers, and stakeholders in designing creative approaches for minimizing the destructive consequences of plastic pollution on marine environments.