AQUATIC TRASH DETECTION AND CLASSIFICATION: A MACHINE LEARNING AND DEEP LEARNING PERSPECTIVE

The escalating volume of pollutants flowing into the oceans and waterways is an alarming concern, not only to marine ecosystems but also to the health and livelihoods of communities worldwide. The rate at which aquatic trash is accumulating far outpaces its’ slow degradation, creating a persistent and growing problem. Both prevention and cleanup are essential for restoring and maintaining healthy aquatic environments. Advanced technology combining machine learning and deep learning algorithms with autonomous underwater vehicles (AUVs) is creating intelligent, automated solutions for detecting and removing trash from the waterways. This approach simplifies the cleanup process and is more efficient than manual methods. This paper examines the crucial role of machine learning and deep learning in detecting various types of aquatic trash. It offers a comprehensive analysis of recent research in the field, comparing different studies based on a variety of parameters. The study also discusses the challenges of trash detection in dynamic aquatic environments, highlighting scope for the future research