  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
