增强的轻量级YOLOX用于在无人机图像中检测小物体野火
Tian Luan1, Shixiong Zhou1, Guokang Zhang1
1College of Air Traffic Managment, Civil Aviation Flight University of China, Guanghan 618307, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究引入了改进的YOLOX网络,用于使用无人机图像更快地检测森林火灾,显著提高了准确性,并减少了早期野火警告的错误检测.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 林业和消防管理
背景情况:
- 无人驾驶飞行器 (UAV) 图像对于森林火灾的检测至关重要,但目前的方法存在遗漏错误,准确性低,早期预警能力差.
- 现有的目标检测算法难以应对火灾环境的复杂性以及无人机平台的小目标检测.
研究的目的:
- 开发一个增强的YOLOX网络,以在无人机捕获的空中图像中快速准确地检测森林火灾.
- 改进小火目标的检测,减少复杂环境中的误报.
主要方法:
- 实施了一种新的多层次特征提取结构 (CSP-ML),以改善小目标火灾区域检测.
- 集成了一个CBAM注意力机制,以最大限度地减少背景噪声干扰.
- 引入了适应性特征提取模块以实现更好的特征融合,并利用CIoU损失函数来增强正样本识别.
主要成果:
- 与标准YOLOX.相比,改进的YOLOX网络表现出卓越的性能,mAP@50和mAP@50_95分别增加了6.4%和2.17%,比标准YOLOX.
- 在多目标和小目标火焰场景中实现了96.3%的mAP,显著优于FasterRCNN,SSD和YOLOv5.
- 显示出较低的遗漏率和更高的检测准确度,在具有挑战性的火灾条件下有效检测小目标.
结论:
- 提议改进的YOLOX网络为无人机的森林火灾检测提供了增强的能力,解决了当前技术的关键局限性.
- 这一进步为基于无人机的森林火灾巡逻和救援行动提供了至关重要的支持,提高了早期预警和响应的有效性.
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