最近的实时空中物体检测方法,性能,优化和有效的设计趋势,用于机载性能:一项调查
Nadin Habash1,2, Ahmad Abu Alqumsan1, Tao Zhou1
1Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本调查审查了边缘设备和无人机的实时空中物体检测算法. 它专注于轻量级设计,硬件优化,并适应大型模型用于车载实时感知.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 对于实时空中感知的日益增长的需求需要有效的机载物体检测.
- 边缘设备和无人机在计算,内存和功率方面存在严重的限制.
研究的目的:
- 为了对受限制的机载环境进行实时空中物体检测算法的调查.
- 分析数据集,挑战和优化技术用于空中物体检测.
主要方法:
- 审查空中和遥感数据集及其相关挑战 (小型物体,尺度变化,复杂的背景).
- 最近关于轻量级骨干,特征表示,多尺度融合和优化变压器的研究的分类.
- 突出显示硬件意识的优化 (量子化,修剪,TensorRT) 和神经架构搜索 (NAS).
主要成果:
- 确定空中物体检测中的关键挑战.
- 对轻量级和高效探测器的算法进步的概述.
- 讨论模型压缩和部署优化,以实现实时性能.
结论:
- 介绍了设计下一代空中物体探测器的统一观点.
- 在无人机上实现高精度和实时性能是通过综合战略可行的.
- 未来的研究方向包括为无人机限制量身定制的自动化NAS和适应大型预训练模型.
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