Author(s)

Jingyu Zhang ¹, Ao Xiang ², Yu Cheng ³, Qin Yang ⁴, Liyang Wang ⁵

Affiliation(s)

¹ The University of Chicago, The Division of the Physical Sciences, Analytics, Chicago, IL, USA
² School of Computer Science & Engineering (School of Cybersecurity), Digital Media Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
³ The Fu Foundation School of Engineering and Applied Science, Operations Research, Columbia University, New York, NY, USA
⁴ School of Integrated Circuit Science and Engineering (Exemplary School of Microelectronics), Microelectronics Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
⁵ Olin Business School, Washington University in St. Louis, St. Louis, MO, Finance

Corresponding Author

Jingyu Zhang

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.

KEYWORDS

Image recognition; deep learning; river and lake float detection

CITE THIS PAPER

Jingyu Zhang, Ao Xiang, Yu Cheng, Qin Yang, Liyang Wang, Research on Detection of Floating Objects in River and Lake Based on AI Image Recognition. Journal of Artificial Intelligence Practice (2024) Vol. 7: 97-106. DOI: http://dx.doi.org/10.23977/jaip.2024.070213.

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