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Updated: Jan 8, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

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YOLO-TME:一种无人机着陆检测算法,适用于极地冰层基站

Songwei Zhang1,2, Yu Zhang3,4, Yinke Dou5,6

  • 1Shanxi Energy Internet Research Institute, Taiyuan, 030024, China. zsw13543660@163.com.

Scientific reports
|December 19, 2025
PubMed
概括

本研究介绍了YOLO-TME,这是一种用于在极地地区无人机 (UAV) 导航的增强深度学习模型. 它在具有挑战性的低可见性条件下显著提高了目标检测的准确性和速度.

关键词:
有效的不对称检测头.雾全球特征金字塔网络基于变压器的卷积.无人机登陆标记器的登陆标记器这就是YOLOv11的意义.

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