Marine debris detection model based on the improved YOLOv5
Jiazhen Liu, Yi Zhou · 2023
With the increasing global marine pollution, the detection and treatment of marine debris is particularly important. With the continuous development of machine learning, the object detection model has become a powerful tool for marine debris disposal. This paper proposes an improved YOLOvS model [1], [2], which changes backbone to mobileNet[3] on the basis of the original YOLOv5s model and introduces an attention mechanism to filter key features. The results show that on the TrashCan dataset[4], the detection precision and recall rate of our model have reached 79% and 63% respectively, and the detection effect has been improved by 9% and 2% compared with YOLOv5, respectively. Compared with the current underwater target detection model YOLOTrashCan[5], which has higher detection accuracy, the [email protected] has been improved by 2.0%, realizing the accurate detection of marine debris in the real underwater environment. The research shows that our model has high precision while reducing model parameters and improving reasoning speed.