基于卷积变换学习的融合框架,用于在无人机中进行规模不变的长期目标检测和跟踪
Fatma S Alrayes1, Nazir Ahmad2, Asma Alshuhail3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|August 2, 2025
概括
这项研究引入了使用无人机 (UAV) 检测和跟踪目标的新模型. 先进的深度融合模型提高了计算机视觉任务的准确性,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 越来越多地用于环境监测和计算机视觉 (CV) 任务,因为它们的移动性.
- 传统的无人机检测模型与规模变化和复杂的环境作斗争,导致错过的检测和错误报警.
- 准确的目标检测和跟踪对于无人机在军事,城市规划和野生动物监测中的应用至关重要.
研究的目的:
- 为无人机开发一种新的长期目标检测和跟踪模型.
- 提高基于无人机的计算机视觉系统在规模变化的环境中的稳定性和准确性.
- 为应对由摄像机运动和环境复杂性引起的无人机识别方面的挑战.
主要方法:
- 拟议的LTTDT-UAVDFCTL模型使用图像预处理与中位数中位数增强的Wiener过器 (MEWF) 来降低噪音.
- 使用YOLOv8进行对象检测,通过深度融合骨干 (VGG16,CapsNet,EfficientNetB7) 进行特征提取.
- 图形卷积神经网络 (GCN) 用于跟踪,通过混合正弦共弦优化算法 (SCWOA) 进行优化.
主要成果:
- 在VisDrone数据集上,LTTDT-UAVDFCTL模型在目标检测和跟踪方面表现出卓越的性能.
- 实现了80.13%的平均平均精度 (mAP),超过了现有的模型.
- 该模型有效地处理尺度变化,提高准确性,同时减少假阳性.
结论:
- 开发的LTTDT-UAVDFCTL模型为无人机的长期目标检测和跟踪提供了强大的解决方案.
- 深度聚变方法在具有挑战性的,规模变化的环境中显著提高了性能.
- 这项研究有助于通过改进检测和跟踪能力来推进基于无人机的计算机视觉应用.
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