Version 1
: Received: 19 June 2024 / Approved: 19 June 2024 / Online: 19 June 2024 (13:04:58 CEST)
How to cite:
V. L. B. Silva, H.; Pereira de Figueiredo, F. A.; B. Mafra, S.; R. da Cruz, M. Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi. Preprints2024, 2024061361. https://doi.org/10.20944/preprints202406.1361.v1
V. L. B. Silva, H.; Pereira de Figueiredo, F. A.; B. Mafra, S.; R. da Cruz, M. Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi. Preprints 2024, 2024061361. https://doi.org/10.20944/preprints202406.1361.v1
V. L. B. Silva, H.; Pereira de Figueiredo, F. A.; B. Mafra, S.; R. da Cruz, M. Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi. Preprints2024, 2024061361. https://doi.org/10.20944/preprints202406.1361.v1
APA Style
V. L. B. Silva, H., Pereira de Figueiredo, F. A., B. Mafra, S., & R. da Cruz, M. (2024). Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi. Preprints. https://doi.org/10.20944/preprints202406.1361.v1
Chicago/Turabian Style
V. L. B. Silva, H., Samuel B. Mafra and Mateus R. da Cruz. 2024 "Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi" Preprints. https://doi.org/10.20944/preprints202406.1361.v1
Abstract
The exponential growth of maritime traffic has brought with it a multitude of business opportunities, while at the same time posing unprecedented challenges in terms of control and surveillance. Traditional methods struggle to cope with the sheer volume and complexity of maritime activities, which calls for innovative solutions. This study presents a comparative analysis of three prominent computing object detection models applied to maritime surveillance. By evaluating their performance metrics in dynamic maritime environments, this study provides information on the most suitable model for effective maritime monitoring applications, attaining an impressive 91\% mean Average Precision (mAP) using the YOLOv5n model.
Keywords
Computer Vision, Object Detection, Internet of Things, Artificial Intelligence, Maritime-Surveillance.
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
