Design and Implementation of an AI Device Meteor Optical Monitoring System Based on Embedded Technology

1. Background SignificanceWith the development of embedded technology and artificial intelligence, new ideas have emerged for meteor monitoring. The combination of embedded AI devices leverages the advantages of both, making portable meteor optical monitoring systems possible. This article explores the design and implementation of an embedded technology-based AI device meteor optical monitoring system, and verifies its effectiveness and performance through simulation experiments, aiming to enhance the real-time and accuracy of meteor monitoring, providing better monitoring methods for astronomical research and more..2. Theoretical PrinciplesThe software module of the meteor optical monitoring system includes the meteor monitoring and data management modules. The meteor monitoring module extracts moving targets using inter-frame difference methods and background elimination, employs Hough transform to filter line segments, and classifies targets using deep learning models such as convolutional neural networks. The data management module is responsible for data storage, implementing orderly storage and efficient retrieval based on designed directory structures categorized by geographical location and device number, supporting meteor monitoring.3. Experimental System and ConditionsThe experimental system includes the software modules of the meteor optical monitoring system, divided into the meteor monitoring module and the data management module. In the meteor monitoring module, moving target extraction uses inter-frame difference methods and background elimination techniques; line segment filtering utilizes Hough transform to extract meteor line segment features from images and sets parameters for filtering; meteor target classification relies on deep learning models such as convolutional neural networks for accurate classification, reducing false positive rates through training and threshold settings. In the data management module, data storage is achieved through designed directory structures for persistent storage of meteor data, including geographical location and device number elements for convenient data retrieval. The entire system operates on embedded AI devices and specific development environments, providing a comprehensive solution for meteor optical monitoring.。4. Experimental Results and AnalysisSimulation experimental environments were established to verify the performance of the meteor optical monitoring system. The results indicate that the system has high accuracy under high brightness and maintains certain robustness under low brightness; it achieves high accuracy for slow and medium-speed meteors, with slightly lower accuracy for fast meteors; it performs well for meteors from all directions; processing speed is reasonable, minimally affected by factors, achieving a minimum of 20fps and a maximum of 30fps, meeting real-time processing requirements. The overall system performance is good, providing a feasible solution for meteor optical monitoring.。5. ConclusionThis article designs an embedded technology-based AI device meteor optical monitoring system and verifies its performance through simulation experiments. The results show that the system has high accuracy, reasonable processing speed, and good stability, meeting the real-time and accuracy requirements for meteor optical monitoring.。

References:

[1] Chen Jianfeng. Design and Implementation of an AI Device Meteor Optical Monitoring System Based on Embedded Technology[J]. China Machinery, 2024,(19):51-54.

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