EMG-YOLO:边缘计算设备的道路裂检测算法
Yan Xing1,2, Xu Han1, Xiaodong Pan3
1School of Transportation and Surveying Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China.
Frontiers in neurorobotics
|July 17, 2024
概括
这项研究介绍了EMG-YOLO,这是一款用于边缘设备的高效道路裂检测算法. 它通过优化其结构和损失函数来增强YOLOv5,提高准确性和减少计算负载,以便更好地维护道路.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路工程工程的道路工程.
背景情况:
- 道路裂减少了基础设施的寿命,手动检测是低效的.
- 现有的YOLOv5模型面临着噪音大数据和对边缘设备的高计算需求的挑战.
- 边缘计算设备需要优化的模型来实时处理传感器数据.
研究的目的:
- 提出一种新的道路裂检测算法,EMG-YOLO,专门为边缘计算环境设计.
- 解决当前模型在处理质量差的传感器数据和计算负担方面的局限性.
- 在资源有限的设备上提高道路裂检测的准确性和效率.
主要方法:
- 在YOLOv5中实现了高效解头,以分离分类和本地化任务,减少计算负载.
- 将损失函数升级为MPDIOU (Minimum Point Distance Intersection over Union),以最大限度地减少边界框点的差异.
- 集成了GCC3模块用于全球上下文建模,取代了传统的卷积以改善特征表示.
主要成果:
- EMG-YOLO在主流算法中表现出优越的性能,使YOLOv5的准确性提高了2.7%.
- 在平均平均精度 (mAP) 中取得了显著的收益,在0.5的IoU值上增加2.9%,在0.9的IoU值上增加0.9%.
- 该模型在边缘计算设备上的复杂环境中显示了增强的检测能力.
结论:
- 在基于边缘的道路裂检测中,EMG-YOLO有效地解决了数据质量差和计算负担问题.
- 该算法提供了更高的准确性和效率,特别是在具有挑战性的现实场景中.
- 进一步的研究可以专注于为边缘AI应用程序开发更轻量级和更有效的对象检测模型.
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