[论文解读] Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition

Jingyu Zhang, Ao Xiang+3 more|arXiv (Cornell University)|Apr 10, 2024

Advanced Technologies in Various Fields被引用 5

一句话总结

本文研究使用深度学习检测河流和湖泊中的漂浮垃圾，比较 SSD、Faster-RCNN 和 YOLOv5，并提出一个用于改进垃圾检测的软硬件检测系统。

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

研究动机与目标

• 促使在水体环境监测中使用 AI 支持的图像识别。
• 探索河流和湖泊中静态与动态漂浮物的特征提取。
• 评估并比较三种主流深度学习模型在漂浮物检测中的表现。
• 设计并实现一个覆盖硬件和软件组件的漂浮物检测系统。
• 通过实验验证评估系统的准确性与效率。

提出的方法

• 分析在水环境中检测静态与动态特征的技术路径。
• 比较三种主流深度学习模型（SSD、Faster-RCNN、YOLOv5）在漂浮物识别中的表现。
• 开发面向河流和湖泊漂浮物的图像采集与处理工作流。
• 设计并实现用于检测系统的硬件平台和软件框架。
• 进行实验验证以评估在准确性和效率方面的性能。

实验结果

研究问题

• RQ1 在河流和湖泊中，哪些深度学习模型（SSD、Faster-RCNN、YOLOv5）对漂浮物检测最有效？
• RQ2 集成的硬件-软件工作流对检测准确性和效率的影响是多少？
• RQ3 如何为动态水域漂浮物场景优化图像采集与处理？
• RQ4 可以就使用 AI 基于的漂浮物检测系统进行水质监测提供哪些指导？

主要发现

• 对漂浮物检测评估了三种主流模型（SSD、Faster-RCNN、YOLOv5）。
• 所提出的包含硬件和软件组件的系统显示出更好的检测性能。
• 实验验证显示，相较于基线方法，漂浮物检测的准确性和效率有所提升。
• 该工作为河流和湖泊的水质监测提供了新的技术途径。