With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring. This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning. By intricately analyzing the technical pathways for detecting static and dynamic features and considering the characteristics of river and lake debris, a comprehensive image acquisition and processing workflow has been developed. The study highlights the application and performance comparison of three mainstream deep learning models -SSD, Faster-RCNN, and YOLOv5- in debris identification. Additionally, a detection system for floating objects has been designed and implemented, encompassing both hardware platform construction and software framework development. Through rigorous experimental validation, the proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes

翻译：随着人工智能技术的快速发展，AI赋能的图像识别已成为应对传统环境监测挑战的有力工具。本研究聚焦于河湖环境中的漂浮物检测，探索了一种基于深度学习的创新方法。通过深入分析静态与动态特征检测的技术路径，并结合河湖垃圾的特性，构建了完整的图像采集与处理流程。研究重点阐述了SSD、Faster-RCNN和YOLOv5三种主流深度学习模型在垃圾识别中的应用及性能对比。此外，设计并实现了一套包含硬件平台搭建与软件框架开发的漂浮物检测系统。通过严格的实验验证，所提系统能够显著提升垃圾检测的准确性与效率，从而为河湖水质监测提供了新的技术途径。