Summary of Research on Detection Of Floating Objects in River and Lake Based on Ai Intelligent Image Recognition, by Jingyu Zhang et al.
Research on Detection of Floating Objects in River and Lake Based on AI Intelligent Image Recognition
by Jingyu Zhang, Ao Xiang, Yu Cheng, Qin Yang, Liyang Wang
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative approach to detecting floating objects in river and lake environments using artificial intelligence (AI)-enabled image recognition. The study develops a comprehensive image acquisition and processing workflow that can detect both static and dynamic features of debris. Three mainstream deep learning models – SSD, Faster-RCNN, and YOLOv5 – are applied and compared for their performance in debris identification. A detection system is designed and implemented, comprising hardware platform construction and software framework development. Experimental validation demonstrates the proposed system’s ability to enhance accuracy and efficiency in debris detection, offering a new technological avenue for water quality monitoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence to help monitor water quality by detecting objects floating on rivers and lakes. The team developed a special way of taking pictures and analyzing them using three different AI models. They tested these models to see which one worked best at identifying debris. The results show that their new system can accurately detect debris, making it a helpful tool for keeping our water clean. |
Keywords
» Artificial intelligence » Deep learning » Faster rcnn
