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增强型YOLOv8船舶检测助力无人水面舰艇实现高级海上监视。

Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance.

作者信息

Haijoub Abdelilah, Hatim Anas, Guerrero-Gonzalez Antonio, Arioua Mounir, Chougdali Khalid

机构信息

Engineering Sciences Laboratory, National School of Applied Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco.

Laboratory of Research on Sustainable and Innovative Technologies (LaRTID), National School of Applied Sciences of Marrakech, Cadi Ayyad University, Marrakech 40000, Morocco.

出版信息

J Imaging. 2024 Nov 24;10(12):303. doi: 10.3390/jimaging10120303.

DOI:10.3390/jimaging10120303
PMID:39728200
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11676501/
Abstract

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring.

摘要

人工智能和机器学习被集成到无人水面舰艇（USV）中，这显著标志着海上监视的发展。本文提出了一种用于检测和跟踪无人水面舰艇的人工智能方法，具体是利用为满足海上监视需求而进行微调的增强版YOLOv8。该系统部署在NVIDIA Jetson TX2平台上，具有创新的架构和为实时操作及能源效率而优化的感知模块。其平均精度均值（mAP）为0.99，展现出卓越的检测精度，运行速度达到17.99帧每秒，同时能耗仅为5.61焦耳。在精度、处理速度和能源效率之间实现了显著平衡，凸显了该系统在大幅提升海上安全、安保及环境监测方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/ba459e9efd65/jimaging-10-00303-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/c0027334099a/jimaging-10-00303-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/b91a04e79dd5/jimaging-10-00303-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/4f1f539aba02/jimaging-10-00303-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/69b710fde78c/jimaging-10-00303-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/21cdeb94f4be/jimaging-10-00303-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/fb9df2daa4ab/jimaging-10-00303-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/ba459e9efd65/jimaging-10-00303-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/c0027334099a/jimaging-10-00303-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/bc33d5fa1849/jimaging-10-00303-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/d804752e9417/jimaging-10-00303-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/445c82e8baa9/jimaging-10-00303-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/b91a04e79dd5/jimaging-10-00303-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/4f1f539aba02/jimaging-10-00303-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/69b710fde78c/jimaging-10-00303-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/21cdeb94f4be/jimaging-10-00303-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/858d/11676501/ba459e9efd65/jimaging-10-00303-g010.jpg

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本文引用的文献

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Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas.基于人工智能识别的自主海洋机器人，用于海洋保护区的永久监测。
Sensors (Basel). 2021 Apr 10;21(8):2664. doi: 10.3390/s21082664.
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Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle.自主水质监测和水面清洁的无人水面艇。
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Sensors (Basel). 2019 Nov 28;19(23):5230. doi: 10.3390/s19235230.
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