Wayne, Fong (2022) Real-Time Vision-Based fish monitoring system using Raspberry PI. Final Year Project, UTAR.
Abstract
In this paper, the studies towards embedded system, machine learning, and the foundation of electronic circuit have been made. In the modern days, there are many aquaculture farms being developed and maintained to produce more fishes for production. To increase and maintain the performance of the farm, many have deployed various approaches to monitor the fishes in the farm. However, the monitoring process sometimes could be dangerous if proper protocol is not followed. Besides, with only raw human power, the effectiveness of the monitoring process could be reduced greatly. To enhance the productivity, the study of the machine learning would be used for developing a fish detection system. This system would be used as a program to provide users a visual monitoring services to detect the fish entity from an image. At the end of the development, the performance of the detection model in terms of detection accuracy is over 82% mAP. To run the detection system, the system is being embedded into an embedded computer called Raspberry Pi. Furthermore, it is still possible to improve the overall system by adding a few more features. In this case, a simple electronic circuit with the controls from an Arduino microcontroller has been designed to simulate on how the overall system could be improved by implementing a temperature and light level monitoring system. With such implementation, this allows user to monitor not only the fishes activity, but also the temperature of the environment to prevent the fishes from various illness and the light level monitoring system could be used as an indication if more light is required for better visual monitoring process. To allow user to use the system from a system, the system has been configured with VNC services for remote access.
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