Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition

TL;DR

This paper presents an AI-based image recognition system utilizing deep learning models to detect floating debris in rivers and lakes, improving water quality monitoring accuracy and efficiency.

Contribution

It introduces a comprehensive detection workflow and compares three deep learning models for floating object identification in aquatic environments.

Findings

YOLOv5 outperforms SSD and Faster-RCNN in detection accuracy

The system significantly improves debris detection efficiency

Experimental results validate the robustness of the proposed approach

Abstract

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