Proceedings of the
The Nineteenth International Conference on Computational Intelligence and Security (CIS 2023)
December 1 – 4, 2023, Haikou, China
Development of a Water Surface Cleaning System Based on Deep Learning
School of Artificial Intelligence, City College of Dongguan, China.
ABSTRACT
Traditional large cleaning ships are bulky, structurally complex, and expensive, making them unable to adapt to small water bodies. This article proposes a water surface cleaning system suitable for small water bodies. This system utilizes advanced image processing and analysis algorithms to automatically identify and remove pollutants on the water surface. It consists of pollutant detection algorithms and cleaning control devices as the core. By collecting image data of the water surface through cameras, the system analyzes and processes the preprocessed images using the YOLOV5 algorithm, identifies pollutants on the water surface, and autonomously carries out the cleaning task. With the integration of deep learning technology and intelligent devices, this water surface cleaning system enables automated and efficient cleaning processes. It can be applied in areas such as lake cleaning and water conservation management. The system has undergone extensive field testing and demonstrated features such as stable operation, high accuracy, and compact size.
Keywords: Deep learning, Water surface cleaning system, YOLOV5, Cleaning device.
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School of Artificial Intelligence, City College of Dongguan, China.
ABSTRACT
Traditional large cleaning ships are bulky, structurally complex, and expensive, making them unable to adapt to small water bodies. This article proposes a water surface cleaning system suitable for small water bodies. This system utilizes advanced image processing and analysis algorithms to automatically identify and remove pollutants on the water surface. It consists of pollutant detection algorithms and cleaning control devices as the core. By collecting image data of the water surface through cameras, the system analyzes and processes the preprocessed images using the YOLOV5 algorithm, identifies pollutants on the water surface, and autonomously carries out the cleaning task. With the integration of deep learning technology and intelligent devices, this water surface cleaning system enables automated and efficient cleaning processes. It can be applied in areas such as lake cleaning and water conservation management. The system has undergone extensive field testing and demonstrated features such as stable operation, high accuracy, and compact size.
Keywords: Deep learning, Water surface cleaning system, YOLOV5, Cleaning device.
Download PDF