LFIR-YOLO:用于红外车辆和行人检测的轻量级模型
Quan Wang1,2, Fengyuan Liu2, Yi Cao1
1School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了LFIR-YOLO,这是一个用于夜间交通的增强红外探测模型. 它提高了准确性,并减少了实时边缘设备部署的复杂性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于可见度低,夜间城市道路场景对可见光成像构成重大挑战.
- 红外图像由于色彩信息,纹理细节和空间分辨率低而受到影响.
- 现有的模型在红外图像中的对比度和遮噪声有限,因此难以应对.
研究的目的:
- 在夜间交通场景中提高红外目标检测精度.
- 开发一个适合在资源有限的边缘设备上实用实时部署的模型.
- 解决红外图像的局限性,包括低信号噪声比和稀疏的细节.
主要方法:
- 增强的YOLOv8骨干与扩展智能残余 (DWR) 模块用于多尺度特征提取.
- 在子中集成的内容引导注意力 (CGA) 机制,用于特征融合和再调制.
- 在检测头上使用了与细节增强卷积 (DEConv) 和组规范 (GN) 的共享卷积策略.
- 集成PIoU v2和自适应值焦点损失 (ATFL) 损失功能,以改善目标脱和收.
主要成果:
- 与YOLOv8.8相比,LFIR-YOLO在FLIR和多光谱数据集上的检测准确度分别提高了4.3%和2.6%.
- 该模型将参数减少了15.5%,计算复杂度减少了34%.
- 展示了增强的特征提取,融合和细节增强功能.
结论:
- 在充满挑战的夜间条件下,LFIR-YOLO显著改善了红外目标检测.
- 该模型的轻量级设计和减少的复杂性使其成为实时边缘部署的理想选择.
- 提议的改进有效地解决了用于物体检测的红外图像固有的局限性.
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