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

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DOI: 10.23977/jaip.2024.070213 | Downloads: 11 | Views: 192

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.

REFERENCES

[1] Dai Y, Su S, Cai L.Design and Implementation of Lane Line Detection Algorithm Based on Image Recognition [J]. Academic Journal of Computing Information Science, 2023, 6(12):8-10.
[2] Afify M H, Mohammed K K, Hassanien E A.Insight into Automatic Image Diagnosis of Ear Conditions Based on Optimized Deep Learning Approach.[J].Annals of biomedical engineering, 2024, 52(4):865-876.
[3] Mustafa N.Use of M-health Application to Figure Out Post-natal Depression, an Evidence-based Study[J].Journal of Advances in Medicine and Medical Research, 2023, 35(24):81-90.
[4] Lingfei S, Feng Z, Junshi X, et al.Scene-level buildings damage recognition based on Cross Conv-Transformer[J]. International Journal of Digital Earth, 2023, 16(2):3987-4007.
[5] Yi Z, Zhigang W.Dynamic visualization simulation of light motion capture in dance image recognition based on IoT wearable devices[J].Optical and Quantum Electronics, 2023, 56(2):11.
[6] Ankit K, Kumar S S, Navin P, et al.A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition[J].Journal of The Institution of Engineers (India): Series B, 2023, 105(1):15-18.
[7] Pan B, Shi X.Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition[J].Remote Sensing, 2023, 15(23):14-16.
[8] Taku T, W C H, C H H.Small molecule modulators of immune pattern recognition receptors.[J].RSC chemical biology, 2023, 4(12):1014-1036.
[9] Ramalho P W, Andrade S M, Matos D A R L, et al.Amphibians of varzea environments and floating meadows of the oxbow lakes of the Middle Purus River, Amazonas, Brazil[J].Biota Neotropica, 2016, 16(1):1-15.
[10] Radu C, Mircea H, Tiberiu M, et al.Automatic Segmentation of Periodontal Tissue Ultrasound Images with Artificial Intelligence: A Novel Method for Improving Dataset Quality[J].Sensors, 2022, 22(19):7101-7105.

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