An Efficient Model for Floating Trash Detection based on YOLOv5s

Abstract

Water pollution become an serious problem in nowadays. The water can be polluted by many factors, including chemicals, trash, bacteria, and parasites. Different with rest pollutants, which need complex experiment to determine the pollution level, trash can be easily to detect by human eye. However, this work may take a numerous cost while monitoring on large area or for long time, which can also easily increase the errors. Therefore, an effective solution need to be explored to reduce not only the cost but also the errors. This paper proposes an efficient model for automatically detection of floating trash based on YOLOv5s. By using a lightweight architecture, our model give a comparative performances with the original model on different benchmarks, which prove the effectiveness of the proposed method. So, our method could be applied to any monitoring or detecting systems with low cost.