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Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

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Updated: Jan 7, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

一个双模拟自适应金字塔转换算法用于无人机跨模拟物体检测.

Qiqin Li¹, Ming Yang^1,2,3, Xiaoqiang Zhang¹

¹College of Aviation Electronics and Electrical, Civil Aviation Flight University of China, Guanghan 618307, China.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

本研究引入了一种用于无人机 (UAV) 的新型双模式自适应金字塔变压器 (DAP) 模块,以改进红外可见图像检测. 在关键应用中,DAP模块在复杂的照明条件下提高了目标识别精度.

关键词:

两种模式的核聚变红外可见图像检测红外可见图像检测目标检测目标检测目标检测变压器变压器变压器变压器无人驾驶飞行器是一种无人驾驶飞行器.

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科学领域:

计算机视觉计算机视觉
人工智能的人工智能
遥感遥感遥感遥感

背景情况:

无人机对于监视和灾害管理至关重要,需要可靠的红外可见图像检测.
现有的无人机检测方法与多尺度目标,照明变化和高效的跨模式数据使用作斗争.
复杂的照明条件对基于无人机的准确目标识别构成重大挑战.

研究的目的:

开发一种轻量级模块,用于增强无人机中的红外可见图像检测.
解决多尺度目标识别和对照明变化的稳定性方面的挑战.
改进跨模式信息的利用,以提高检测准确度.

主要方法:

提出了一种轻量级的双模式自适应金字塔变压器 (DAP) 模块.
将DAP模块集成到YOLOv8对象检测框架中.
采用分层自我注意和残余融合,用于自适应的多尺度表示和跨模式对齐.

主要成果:

基于DAP的YOLOv8在无人机车辆上获得了61.2%的mAP50:95分数,在LLVIP数据集上达到62.1%.
与传统的红外可见探测方法相比,其表现卓越.
在复杂的环境和具有挑战性的照明中验证了模块的有效性.

结论:

DAP模块有效地优化无人机红外可见检测的跨模式功能交互.
拟议的方法为实时无人机应用提供了实用和高效的解决方案.
增强的检测准确性提高了无人机在交通监控和灾难响应方面的能力.