基于人工智能智能的物联网视觉图像设计
1College of Fine Arts and Design, Mudanjiang Normal University, Mudanjiang, 157011, Heilongjiang, China.
Heliyon
|December 21, 2023
概括
这项研究引入了一个新的AI模型,DSYolov3与MFCAM,用于无人机 (UAV) 视觉物体检测. 它实现了高精度和高效率,使无人机自主操作成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 无人驾驶飞行器 (UAV) 在各个领域的自主运营中变得越来越重要.
- 有效的视觉物体检测对于无人机收集数据和做出决定至关重要.
- 挑战包括空中图像中的不同物体尺寸和有限的无人机通信范围.
研究的目的:
- 为无人机应用中强大的视觉物体检测提出一种新的方法.
- 为了应对空中图像中尺度变量物体识别的挑战.
- 通过改进图像分析,提高无人机的自主性和效率.
主要方法:
- 开发了DSYolov3模型,与一个新的多尺度融合通道注意力 (MFCAM) 模块集成.
- 通过空间金字塔聚合,MFCAM在多个尺度上融合了通道明智的特征.
- 该方法是为UAV特定的尺度变量对象识别任务而设计的.
主要成果:
- 拟议的方法在实验评估中表现出高的有效性和效率.
- 在视觉图像检测方面取得了95%的成功率.
- 报告了97%的准确率和94%的计算成本成功率.
结论:
- 具有MFCAM模块的新型DSYolov3显著改善了无人机的视觉物体检测.
- 与现有的探测器相比,该方法提供了更高的性能.
- 开发的模型适合在无人机上部署,提高它们的作战能力.
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