Enhanced autonomous water garbage collection system using deep learning-based object detection and path planning

Abstract

Water pollution, particularly from floating debris such as plastics, has become a critical environmental issue, threatening aquatic ecosystems and biodiversity. Autonomous solutions for the detection and removal of waste are increasingly essential for maintaining water cleanliness and mitigating pollution. However, existing systems face limitations in real-time detection, accuracy, and adaptability to diverse aquatic environments. This paper utilizes the water pollution images dataset, comprising almost 300 high-resolution images from lakes, rivers, and coastal areas, representing various types of floating waste under different environmental conditions. In response to these challenges, this paper introduces an autonomous unmanned surface vehicle (USV) system equipped with the enhanced waste detection network (EWD-Net). EWD-Net improves upon traditional single-shot detection algorithms by integrating deeper feature extraction layers and enhancing computational efficiency, resulting in higher accuracy and faster detection. Additionally, the system includes the dynamic path optimization (DPO) module for efficient navigation and obstacle avoidance in complex water environments. The novelty of this system lies in its dual approach, combining advanced detection with optimized path planning, ensuring effective autonomous operation. The results indicate that the proposed model achieves an accuracy of 94.6%, outperforming existing algorithms and providing a robust solution for real-time waste detection and collection.